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Li S, Xu M, Meng Y, Sun H, Zhang T, Yang H, Li Y, Ma X. The application of the combination between artificial intelligence and endoscopy in gastrointestinal tumors. MEDCOMM – ONCOLOGY 2024; 3. [DOI: 10.1002/mog2.91] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 09/03/2024] [Indexed: 01/04/2025]
Abstract
AbstractGastrointestinal (GI) tumors have always been a major type of malignant tumor and a leading cause of tumor‐related deaths worldwide. The main principles of modern medicine for GI tumors are early prevention, early diagnosis, and early treatment, with early diagnosis being the most effective measure. Endoscopy, due to its ability to visualize lesions, has been one of the primary modalities for screening, diagnosing, and treating GI tumors. However, a qualified endoscopist often requires long training and extensive experience, which to some extent limits the wider use of endoscopy. With advances in data science, artificial intelligence (AI) has brought a new development direction for the endoscopy of GI tumors. AI can quickly process large quantities of data and images and improve diagnostic accuracy with some training, greatly reducing the workload of endoscopists and assisting them in early diagnosis. Therefore, this review focuses on the combined application of endoscopy and AI in GI tumors in recent years, describing the latest research progress on the main types of tumors and their performance in clinical trials, the application of multimodal AI in endoscopy, the development of endoscopy, and the potential applications of AI within it, with the aim of providing a reference for subsequent research.
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Affiliation(s)
- Shen Li
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
| | - Maosen Xu
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy, West China Hospital, National Clinical Research, Sichuan University Chengdu Sichuan China
| | - Yuanling Meng
- West China School of Stomatology Sichuan University Chengdu Sichuan China
| | - Haozhen Sun
- College of Life Sciences Sichuan University Chengdu Sichuan China
| | - Tao Zhang
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
| | - Hanle Yang
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
| | - Yueyi Li
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
| | - Xuelei Ma
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
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Zhou Y, Zhou L, Yan J, Yan X, Chen Z. Using optical coherence tomography to assess luster of pearls: technique suitability and insights. Sci Rep 2024; 14:11126. [PMID: 38750292 PMCID: PMC11096156 DOI: 10.1038/s41598-024-62125-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 05/14/2024] [Indexed: 05/18/2024] Open
Abstract
Luster is one of the vital indexes in pearl grading. To find a fast, nondestructive, and low-cost grading method, optical coherence tomography (OCT) is introduced to predict the luster grade through the texture features. After background removal, flattening, and segmentation, the speckle pattern of the region of interest is described by seven kinds of feature textures, including center-symmetric auto-correlation (CSAC), fractal dimension (FD), Gabor, gray level co-occurrence matrix (GLCM), histogram of oriented gradients (HOG), laws texture energy (LAWS), and local binary patterns (LBP). To find the relations between speckle-derived texture features and luster grades, four Four groups of pearl samples were used in the experiment to detect texture differences based on support vector machines (SVMs) and random forest classifier (RFC)) for investigating the relations between speckle-derived texture features and luster grades. The precision, recall, F1-score, and accuracy are more significant than 0.9 in several simulations, even after dimension reduction. This demonstrates that the texture feature from OCT images can be applied to class the pearl luster based on speckle changes.
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Affiliation(s)
- Yang Zhou
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, Zhejiang, China.
- School of Innovation and Entrepreneurship, Zhejiang University of Science and Technology, Hangzhou, 310023, Zhejiang, China.
| | - Lifeng Zhou
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, Zhejiang, China
| | - Jun Yan
- Zhejiang Fangyuan Test Group Co., Ltd, Hangzhou, 310013, Zhejiang, China
| | - Xuejun Yan
- Zhejiang Fangyuan Test Group Co., Ltd, Hangzhou, 310013, Zhejiang, China
| | - Zhengwei Chen
- School of Innovation and Entrepreneurship, Zhejiang University of Science and Technology, Hangzhou, 310023, Zhejiang, China
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Guidozzi N, Menon N, Chidambaram S, Markar SR. The role of artificial intelligence in the endoscopic diagnosis of esophageal cancer: a systematic review and meta-analysis. Dis Esophagus 2023; 36:doad048. [PMID: 37480192 PMCID: PMC10789250 DOI: 10.1093/dote/doad048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/23/2023]
Abstract
Early detection of esophageal cancer is limited by accurate endoscopic diagnosis of subtle macroscopic lesions. Endoscopic interpretation is subject to expertise, diagnostic skill, and thus human error. Artificial intelligence (AI) in endoscopy is increasingly bridging this gap. This systematic review and meta-analysis consolidate the evidence on the use of AI in the endoscopic diagnosis of esophageal cancer. The systematic review was carried out using Pubmed, MEDLINE and Ovid EMBASE databases and articles on the role of AI in the endoscopic diagnosis of esophageal cancer management were included. A meta-analysis was also performed. Fourteen studies (1590 patients) assessed the use of AI in endoscopic diagnosis of esophageal squamous cell carcinoma-the pooled sensitivity and specificity were 91.2% (84.3-95.2%) and 80% (64.3-89.9%). Nine studies (478 patients) assessed AI capabilities of diagnosing esophageal adenocarcinoma with the pooled sensitivity and specificity of 93.1% (86.8-96.4) and 86.9% (81.7-90.7). The remaining studies formed the qualitative summary. AI technology, as an adjunct to endoscopy, can assist in accurate, early detection of esophageal malignancy. It has shown superior results to endoscopists alone in identifying early cancer and assessing depth of tumor invasion, with the added benefit of not requiring a specialized skill set. Despite promising results, the application in real-time endoscopy is limited, and further multicenter trials are required to accurately assess its use in routine practice.
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Affiliation(s)
- Nadia Guidozzi
- Department of General Surgery, University of Witwatersrand, Johannesburg, South Africa
| | - Nainika Menon
- Department of General Surgery, Oxford University Hospitals, Oxford, UK
| | - Swathikan Chidambaram
- Academic Surgical Unit, Department of Surgery and Cancer, Imperial College London, St Mary’s Hospital, London, UK
| | - Sheraz Rehan Markar
- Department of General Surgery, Oxford University Hospitals, Oxford, UK
- Nuffield Department of Surgery, University of Oxford, Oxford, UK
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Uno K, Koike T, Hatta W, Saito M, Tanabe M, Masamune A. Development of Advanced Imaging and Molecular Imaging for Barrett's Neoplasia. Diagnostics (Basel) 2022; 12:2437. [PMID: 36292126 PMCID: PMC9600913 DOI: 10.3390/diagnostics12102437] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 10/04/2022] [Indexed: 11/17/2022] Open
Abstract
Barrett esophagus (BE) is a precursor to a life-threatening esophageal adenocarcinoma (EAC). Surveillance endoscopy with random biopsies is recommended for early intervention against EAC, but its adherence in the clinical setting is poor. Dysplastic lesions with flat architecture and patchy distribution in BE are hardly detected by high-resolution endoscopy, and the surveillance protocol entails issues of time and labor and suboptimal interobserver agreement for diagnosing dysplasia. Therefore, the development of advanced imaging technologies is necessary for Barrett's surveillance. Recently, non-endoscopic or endoscopic technologies, such as cytosponge, endocytoscopy, confocal laser endomicroscopy, autofluorescence imaging, and optical coherence tomography/volumetric laser endomicroscopy, were developed, but most of them are not clinically available due to the limited view field, expense of the equipment, and significant time for the learning curve. Another strategy is focused on the development of molecular biomarkers, which are also not ready to use. However, a combination of advanced imaging techniques together with specific biomarkers is expected to identify morphological abnormalities and biological disorders at an early stage in the surveillance. Here, we review recent developments in advanced imaging and molecular imaging for Barrett's neoplasia. Further developments in multiple biomarker panels specific for Barrett's HGD/EAC include wide-field imaging systems for targeting 'red flags', a high-resolution imaging system for optical biopsy, and a computer-aided diagnosis system with artificial intelligence, all of which enable a real-time and accurate diagnosis of dysplastic BE in Barrett's surveillance and provide information for precision medicine.
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Affiliation(s)
- Kaname Uno
- Division of Gastroenterology, Tohoku University Hospital, Sendai 981-8574, Japan
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Luo H, Li S, Zeng Y, Cheema H, Otegbeye E, Ahmed S, Chapman WC, Mutch M, Zhou C, Zhu Q. Human colorectal cancer tissue assessment using optical coherence tomography catheter and deep learning. JOURNAL OF BIOPHOTONICS 2022; 15:e202100349. [PMID: 35150067 PMCID: PMC9581715 DOI: 10.1002/jbio.202100349] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 01/16/2022] [Accepted: 02/09/2022] [Indexed: 05/02/2023]
Abstract
Optical coherence tomography (OCT) can differentiate normal colonic mucosa from neoplasia, potentially offering a new mechanism of endoscopic tissue assessment and biopsy targeting, with a high optical resolution and an imaging depth of ~1 mm. Recent advances in convolutional neural networks (CNN) have enabled application in ophthalmology, cardiology, and gastroenterology malignancy detection with high sensitivity and specificity. Here, we describe a miniaturized OCT catheter and a residual neural network (ResNet)-based deep learning model manufactured and trained to perform automatic image processing and real-time diagnosis of the OCT images. The OCT catheter has an outer diameter of 3.8 mm, a lateral resolution of ~7 μm, and an axial resolution of ~6 μm. A customized ResNet is utilized to classify OCT catheter colorectal images. An area under the receiver operating characteristic (ROC) curve (AUC) of 0.975 is achieved to distinguish between normal and cancerous colorectal tissue images.
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Affiliation(s)
- Hongbo Luo
- Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Shuying Li
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Yifeng Zeng
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Hassam Cheema
- Department of Anatomic & Molecular Pathology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Ebunoluwa Otegbeye
- Department of Surgery, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Safee Ahmed
- Department of Anatomic & Clinical Pathology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - William C. Chapman
- Department of Surgery, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Matthew Mutch
- Department of Surgery, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Chao Zhou
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Quing Zhu
- Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA
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Moradi M, Du X, Huan T, Chen Y. Feasibility of the soft attention-based models for automatic segmentation of OCT kidney images. BIOMEDICAL OPTICS EXPRESS 2022; 13:2728-2738. [PMID: 35774323 PMCID: PMC9203082 DOI: 10.1364/boe.449942] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/22/2022] [Accepted: 02/18/2022] [Indexed: 06/15/2023]
Abstract
Clinically, optical coherence tomography (OCT) has been utilized to obtain the images of the kidney's proximal convoluted tubules (PCTs), which can be used to quantify the morphometric parameters such as tubular density and diameter. Such parameters are useful for evaluating the status of the donor kidney for transplant. Quantifying PCTs from OCT images by human readers is a time-consuming and tedious process. Despite the fact that conventional deep learning models such as conventional neural networks (CNNs) have achieved great success in the automatic segmentation of kidney OCT images, gaps remain regarding the segmentation accuracy and reliability. Attention-based deep learning model has benefits over regular CNNs as it is intended to focus on the relevant part of the image and extract features for those regions. This paper aims at developing an Attention-based UNET model for automatic image analysis, pattern recognition, and segmentation of kidney OCT images. We evaluated five methods including the Residual-Attention-UNET, Attention-UNET, standard UNET, Residual UNET, and fully convolutional neural network using 14403 OCT images from 169 transplant kidneys for training and testing. Our results show that Residual-Attention-UNET outperformed the other four methods in segmentation by showing the highest values of all the six metrics including dice score (0.81 ± 0.01), intersection over union (IOU, 0.83 ± 0.02), specificity (0.84 ± 0.02), recall (0.82 ± 0.03), precision (0.81 ± 0.01), and accuracy (0.98 ± 0.08). Our results also show that the performance of the Residual-Attention-UNET is equivalent to the human manual segmentation (dice score = 0.84 ± 0.05). Residual-Attention-UNET and Attention-UNET also demonstrated good performance when trained on a small dataset (3456 images) whereas the performance of the other three methods dropped dramatically. In conclusion, our results suggested that the soft Attention-based models and specifically Residual-Attention-UNET are powerful and reliable methods for tubule lumen identification and segmentation and can help clinical evaluation of transplant kidney viability as fast and accurate as possible.
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Affiliation(s)
- Mousa Moradi
- Department of Biomedical Engineering, University of Massachusetts, Amherst, MA 01003, USA
- Institute for Applied Life Sciences, University of Massachusetts, Amherst, MA 01003, USA
| | - Xian Du
- Institute for Applied Life Sciences, University of Massachusetts, Amherst, MA 01003, USA
- Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, MA 01003, USA
| | - Tianxiao Huan
- Department of Ophthalmology & Visual Sciences, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA
| | - Yu Chen
- Department of Biomedical Engineering, University of Massachusetts, Amherst, MA 01003, USA
- Institute for Applied Life Sciences, University of Massachusetts, Amherst, MA 01003, USA
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Ghalati MK, Nunes A, Ferreira H, Serranho P, Bernardes R. Texture Analysis and its Applications in Biomedical Imaging: A Survey. IEEE Rev Biomed Eng 2021; 15:222-246. [PMID: 34570709 DOI: 10.1109/rbme.2021.3115703] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This surveys emphasis is in collecting and categorising over five decades of active research on texture analysis. Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this surveys final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.
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Yang Z, Shang J, Liu C, Zhang J, Liang Y. Identification of oral precancerous and cancerous tissue by swept source optical coherence tomography. Lasers Surg Med 2021; 54:320-328. [PMID: 34342365 DOI: 10.1002/lsm.23461] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/11/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND OBJECTIVES Distinguishing cancer from precancerous lesions is critical and challenging in oral medicine. As a noninvasive method, optical coherence tomography (OCT) has the advantages of real-time, in vivo, and large-depth imaging. Texture information hidden in OCT images can provide an important auxiliary effect for improving diagnostic accuracy. The aim of this study is to explore a reliable and accurate OCT-based method for the screening and diagnosis of human oral diseases, especially oral cancer. MATERIALS AND METHODS Fresh ex vivo oral tissues including normal mucosa, leukoplakia with epithelial hyperplasia (LEH), and oral squamous cell carcinoma (OSCC) were imaged intraoperatively by a homemade OCT system, and 58 texture features were extracted to create computational models of these tissues. A principal component analysis algorithm was employed to optimize the combination of texture feature vectors. The identification based on artificial neural network (ANN) was proposed and the sensitivity/specificity was calculated statistically to evaluate the classification performance. RESULTS A total of 71 sites of three types of oral tissues were measured, and 5176 OCT images of three types of oral tissues were used in this study. The superior classification result based on ANN was obtained with an average accuracy of 98.17%. The sensitivity and specificity of normal mucosa, LEH, and OSCC are 98.17% / 98.38%, 93.81% / 98.54%, and 98.11% / 99.04%, respectively. CONCLUSION It is demonstrated from the high accuracies, sensitivities, and specificities that texture-based analysis can be used to identify oral precancerous and cancerous tissue in OCT images, and it has the potential to help surgeons in diseases screening and diagnosis effectively.
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Affiliation(s)
- Zihan Yang
- Institute of Modern Optics, Nankai University, Tianjin Key Laboratory of Micro-Scale Optical Information Science and Technology, Tianjin, China
| | - Jianwei Shang
- Department of Oral Pathology, Tianjin Stomatological Hospital, Hospital of Stomatology, Nankai University, Tianjin, China
| | - Chenlu Liu
- Department of Oral Medicine, Tianjin Stomatological Hospital, Hospital of Stomatology, Nankai University, Tianjin, China
| | - Jun Zhang
- Department of Oral-Maxillofacial Surgery, Tianjin Stomatological Hospital, Hospital of Stomatology, Nankai University, Tianjin, China
| | - Yanmei Liang
- Institute of Modern Optics, Nankai University, Tianjin Key Laboratory of Micro-Scale Optical Information Science and Technology, Tianjin, China
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Yang Z, Shang J, Liu C, Zhang J, Liang Y. Classification of oral salivary gland tumors based on texture features in optical coherence tomography images. Lasers Med Sci 2021; 37:1139-1146. [PMID: 34185166 DOI: 10.1007/s10103-021-03365-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Accepted: 06/21/2021] [Indexed: 10/21/2022]
Abstract
Currently, the diagnoses of oral diseases primarily depend on the visual recognition of experienced clinicians. It has been proven that automatic recognition based on images can support clinical decision-making by extracting and analyzing objective hidden information. In recent years, optical coherence tomography (OCT) has become a powerful optical imaging technique with the advantages of high resolution and non-invasion. In our study, a dataset composed of four kinds of oral salivary gland tumors (SGTs) was obtained from a homemade swept-source OCT, including two benign and two malignant tumors. Seventy-six texture features were extracted from OCT images to create computational models of diseases. It was demonstrated that the artificial neural network (ANN) based on principal component analysis (PCA) can obtain high diagnostic sensitivity and specificity (higher than 99%) for these four kinds of tumors. The classification accuracy of each tumor is larger than 99%. In addition, the performances of two classifiers (ANN and support vector machine) were quantitatively evaluated based on SGTs. It was proven that the texture features in OCT images provided objective information to classify oral tumors.
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Affiliation(s)
- Zihan Yang
- Tianjin Key Laboratory of Micro-scale Optical Information Science and Technology, Institute of Modern Optics, Nankai University, 38 Tongyan Road, Tianjin, 300350, China
| | - Jianwei Shang
- Department of Oral Pathology, Tianjin Stomatological Hospital, Hospital of Stomatology, Nankai University, Tianjin, 300041, China
| | - Chenlu Liu
- Department of Oral Medicine, Tianjin Stomatological Hospital, Hospital of Stomatology, Nankai University, Tianjin, 300041, China
| | - Jun Zhang
- Department of Oral-Maxillofacial Surgery, Tianjin Stomatological Hospital, Hospital of Stomatology, Nankai University, Tianjin, 300041, China
| | - Yanmei Liang
- Tianjin Key Laboratory of Micro-scale Optical Information Science and Technology, Institute of Modern Optics, Nankai University, 38 Tongyan Road, Tianjin, 300350, China.
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Wang C, Gan M. Tissue self-attention network for the segmentation of optical coherence tomography images on the esophagus. BIOMEDICAL OPTICS EXPRESS 2021; 12:2631-2646. [PMID: 34123493 PMCID: PMC8176794 DOI: 10.1364/boe.419809] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 04/01/2021] [Accepted: 04/01/2021] [Indexed: 05/06/2023]
Abstract
Automatic segmentation of layered tissue is the key to esophageal optical coherence tomography (OCT) image processing. With the advent of deep learning techniques, frameworks based on a fully convolutional network are proved to be effective in classifying pixels on images. However, due to speckle noise and unfavorable imaging conditions, the esophageal tissue relevant to the diagnosis is not always easy to identify. An effective approach to address this problem is extracting more powerful feature maps, which have similar expressions for pixels in the same tissue and show discriminability from those from different tissues. In this study, we proposed a novel framework, called the tissue self-attention network (TSA-Net), which introduces the self-attention mechanism for esophageal OCT image segmentation. The self-attention module in the network is able to capture long-range context dependencies from the image and analyzes the input image in a global view, which helps to cluster pixels in the same tissue and reveal differences of different layers, thus achieving more powerful feature maps for segmentation. Experiments have visually illustrated the effectiveness of the self-attention map, and its advantages over other deep networks were also discussed.
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Affiliation(s)
- Cong Wang
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Meng Gan
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
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Automated software-assisted diagnosis of esophageal squamous cell neoplasia using high-resolution microendoscopy. Gastrointest Endosc 2021; 93:831-838.e2. [PMID: 32682812 PMCID: PMC7855348 DOI: 10.1016/j.gie.2020.07.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 07/02/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS High-resolution microendoscopy (HRME) is an optical biopsy technology that provides subcellular imaging of esophageal mucosa but requires expert interpretation of these histopathology-like images. We compared endoscopists with an automated software algorithm for detection of esophageal squamous cell neoplasia (ESCN) and evaluated the endoscopists' accuracy with and without input from the software algorithm. METHODS Thirteen endoscopists (6 experts, 7 novices) were trained and tested on 218 post-hoc HRME images from 130 consecutive patients undergoing ESCN screening/surveillance. The automated software algorithm interpreted all images as neoplastic (high-grade dysplasia, ESCN) or non-neoplastic. All endoscopists provided their interpretation (neoplastic or non-neoplastic) and confidence level (high or low) without and with knowledge of the software overlay highlighting abnormal nuclei and software interpretation. The criterion standard was histopathology consensus diagnosis by 2 pathologists. RESULTS The endoscopists had a higher mean sensitivity (84.3%, standard deviation [SD] 8.0% vs 76.3%, P = .004), lower specificity (75.0%, SD 5.2% vs 85.3%, P < .001) but no significant difference in accuracy (81.1%, SD 5.2% vs 79.4%, P = .26) of ESCN detection compared with the automated software algorithm. With knowledge of the software algorithm, the specificity of the endoscopists increased significantly (75.0% to 80.1%, P = .002) but not the sensitivity (84.3% to 84.8%, P = .75) or accuracy (81.1% to 83.1%, P = .13). The increase in specificity was among novices (P = .008) but not experts (P = .11). CONCLUSIONS The software algorithm had lower sensitivity but higher specificity for ESCN detection than endoscopists. Using computer-assisted diagnosis, the endoscopists maintained high sensitivity while increasing their specificity and accuracy compared with their initial diagnosis. Automated HRME interpretation would facilitate widespread usage in resource-poor areas where this portable, low-cost technology is needed.
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Huang LM, Yang WJ, Huang ZY, Tang CW, Li J. Artificial intelligence technique in detection of early esophageal cancer. World J Gastroenterol 2020; 26:5959-5969. [PMID: 33132647 PMCID: PMC7584056 DOI: 10.3748/wjg.v26.i39.5959] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 08/22/2020] [Accepted: 09/04/2020] [Indexed: 02/06/2023] Open
Abstract
Due to the rapid progression and poor prognosis of esophageal cancer (EC), the early detection and diagnosis of early EC are of great value for the prognosis improvement of patients. However, the endoscopic detection of early EC, especially Barrett's dysplasia or squamous epithelial dysplasia, is difficult. Therefore, the requirement for more efficient methods of detection and characterization of early EC has led to intensive research in the field of artificial intelligence (AI). Deep learning (DL) has brought about breakthroughs in processing images, videos, and other aspects, whereas convolutional neural networks (CNNs) have shone lights on detection of endoscopic images and videos. Many studies on CNNs in endoscopic analysis of early EC demonstrate excellent performance including sensitivity and specificity and progress gradually from in vitro image analysis for classification to real-time detection of early esophageal neoplasia. When AI technique comes to the pathological diagnosis, borderline lesions that are difficult to determine may become easier than before. In gene diagnosis, due to the lack of tissue specificity of gene diagnostic markers, they can only be used as supplementary measures at present. In predicting the risk of cancer, there is still a lack of prospective clinical research to confirm the accuracy of the risk stratification model.
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Affiliation(s)
- Lu-Ming Huang
- Department of Gastroenterology, West China Hospital Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Wen-Juan Yang
- Department of Gastroenterology, West China Hospital Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Zhi-Yin Huang
- Department of Gastroenterology, West China Hospital Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Cheng-Wei Tang
- Department of Gastroenterology, West China Hospital Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Jing Li
- Department of Gastroenterology, West China Hospital Sichuan University, Chengdu 610041, Sichuan Province, China
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Zhang YH, Guo LJ, Yuan XL, Hu B. Artificial intelligence-assisted esophageal cancer management: Now and future. World J Gastroenterol 2020; 26:5256-5271. [PMID: 32994686 PMCID: PMC7504247 DOI: 10.3748/wjg.v26.i35.5256] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 07/29/2020] [Accepted: 08/12/2020] [Indexed: 02/06/2023] Open
Abstract
Esophageal cancer poses diagnostic, therapeutic and economic burdens in high-risk regions. Artificial intelligence (AI) has been developed for diagnosis and outcome prediction using various features, including clinicopathologic, radiologic, and genetic variables, which can achieve inspiring results. One of the most recent tasks of AI is to use state-of-the-art deep learning technique to detect both early esophageal squamous cell carcinoma and esophageal adenocarcinoma in Barrett’s esophagus. In this review, we aim to provide a comprehensive overview of the ways in which AI may help physicians diagnose advanced cancer and make clinical decisions based on predicted outcomes, and combine the endoscopic images to detect precancerous lesions or early cancer. Pertinent studies conducted in recent two years have surged in numbers, with large datasets and external validation from multi-centers, and have partly achieved intriguing results of expert’s performance of AI in real time. Improved pre-trained computer-aided diagnosis algorithms in the future studies with larger training and external validation datasets, aiming at real-time video processing, are imperative to produce a diagnostic efficacy similar to or even superior to experienced endoscopists. Meanwhile, supervised randomized controlled trials in real clinical practice are highly essential for a solid conclusion, which meets patient-centered satisfaction. Notably, ethical and legal issues regarding the black-box nature of computer algorithms should be addressed, for both clinicians and regulators.
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Affiliation(s)
- Yu-Hang Zhang
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Lin-Jie Guo
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Xiang-Lei Yuan
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bing Hu
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
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14
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Wang C, Gan M, Zhang M, Li D. Adversarial convolutional network for esophageal tissue segmentation on OCT images. BIOMEDICAL OPTICS EXPRESS 2020; 11:3095-3110. [PMID: 32637244 PMCID: PMC7316031 DOI: 10.1364/boe.394715] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 05/08/2020] [Accepted: 05/08/2020] [Indexed: 05/20/2023]
Abstract
Automatic segmentation is important for esophageal OCT image processing, which is able to provide tissue characteristics such as shape and thickness for disease diagnosis. Existing automatical segmentation methods based on deep convolutional networks may not generate accurate segmentation results due to limited training set and various layer shapes. This study proposed a novel adversarial convolutional network (ACN) to segment esophageal OCT images using a convolutional network trained by adversarial learning. The proposed framework includes a generator and a discriminator, both with U-Net alike fully convolutional architecture. The discriminator is a hybrid network that discriminates whether the generated results are real and implements pixel classification at the same time. Leveraging on the adversarial training, the discriminator becomes more powerful. In addition, the adversarial loss is able to encode high order relationships of pixels, thus eliminating the requirements of post-processing. Experiments on segmenting esophageal OCT images from guinea pigs confirmed that the ACN outperforms several deep learning frameworks in pixel classification accuracy and improves the segmentation result. The potential clinical application of ACN for detecting eosinophilic esophagitis (EoE), an esophageal disease, is also presented in the experiment.
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Affiliation(s)
- Cong Wang
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- These authors contributed equally to this work and should be considered co-first authors
| | - Meng Gan
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- These authors contributed equally to this work and should be considered co-first authors
| | - Miao Zhang
- School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China
| | - Deyin Li
- School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China
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15
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Wang C, Gan M, Zhang M, Li D. Adversarial convolutional network for esophageal tissue segmentation on OCT images. BIOMEDICAL OPTICS EXPRESS 2020; 11:3095-3110. [PMID: 32637244 DOI: 10.1109/access.2020.3041767] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 05/08/2020] [Accepted: 05/08/2020] [Indexed: 05/26/2023]
Abstract
Automatic segmentation is important for esophageal OCT image processing, which is able to provide tissue characteristics such as shape and thickness for disease diagnosis. Existing automatical segmentation methods based on deep convolutional networks may not generate accurate segmentation results due to limited training set and various layer shapes. This study proposed a novel adversarial convolutional network (ACN) to segment esophageal OCT images using a convolutional network trained by adversarial learning. The proposed framework includes a generator and a discriminator, both with U-Net alike fully convolutional architecture. The discriminator is a hybrid network that discriminates whether the generated results are real and implements pixel classification at the same time. Leveraging on the adversarial training, the discriminator becomes more powerful. In addition, the adversarial loss is able to encode high order relationships of pixels, thus eliminating the requirements of post-processing. Experiments on segmenting esophageal OCT images from guinea pigs confirmed that the ACN outperforms several deep learning frameworks in pixel classification accuracy and improves the segmentation result. The potential clinical application of ACN for detecting eosinophilic esophagitis (EoE), an esophageal disease, is also presented in the experiment.
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Affiliation(s)
- Cong Wang
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- These authors contributed equally to this work and should be considered co-first authors
| | - Meng Gan
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- These authors contributed equally to this work and should be considered co-first authors
| | - Miao Zhang
- School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China
| | - Deyin Li
- School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China
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16
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Gulati S, Patel M, Emmanuel A, Haji A, Hayee B, Neumann H. The future of endoscopy: Advances in endoscopic image innovations. Dig Endosc 2020; 32:512-522. [PMID: 31286574 DOI: 10.1111/den.13481] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Accepted: 07/01/2019] [Indexed: 02/08/2023]
Abstract
The latest state of the art technological innovations have led to a palpable progression in endoscopic imaging and may facilitate standardisation of practice. One of the most rapidly evolving modalities is artificial intelligence with recent studies providing real-time diagnoses and encouraging results in the first randomised trials to conventional endoscopic imaging. Advances in functional hypoxia imaging offer novel opportunities to be used to detect neoplasia and the assessment of colitis. Three-dimensional volumetric imaging provides spatial information and has shown promise in the increased detection of small polyps. Studies to date of self-propelling colonoscopes demonstrate an increased caecal intubation rate and possibly offer patients a more comfortable procedure. Further development in robotic technology has introduced ex vivo automated locomotor upper gastrointestinal and small bowel capsule devices. Eye-tracking has the potential to revolutionise endoscopic training through the identification of differences in experts and non-expert endoscopist as trainable parameters. In this review, we discuss the latest innovations of all these technologies and provide perspective into the exciting future of diagnostic luminal endoscopy.
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Affiliation(s)
- Shraddha Gulati
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London, UK
| | - Mehul Patel
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London, UK
| | - Andrew Emmanuel
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London, UK
| | - Amyn Haji
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London, UK
| | - Bu'Hussain Hayee
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London, UK
| | - Helmut Neumann
- Department of Medicine, University Hospital Mainz, Mainz, Germany
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17
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Hoogenboom SA, Bagci U, Wallace MB. Artificial intelligence in gastroenterology. The current state of play and the potential. How will it affect our practice and when? ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.tgie.2019.150634] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Deep principal dimension encoding for the classification of early neoplasia in Barrett's Esophagus with volumetric laser endomicroscopy. Comput Med Imaging Graph 2020; 80:101701. [PMID: 32044547 DOI: 10.1016/j.compmedimag.2020.101701] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 12/20/2019] [Accepted: 01/14/2020] [Indexed: 02/07/2023]
Abstract
Barrett cancer is a treatable disease when detected at an early stage. However, current screening protocols are often not effective at finding the disease early. Volumetric Laser Endomicroscopy (VLE) is a promising new imaging tool for finding dysplasia in Barrett's esophagus (BE) at an early stage, by acquiring cross-sectional images of the microscopic structure of BE up to 3-mm deep. However, interpretation of VLE scans is difficult for medical doctors due to both the size and subtlety of the gray-scale data. Therefore, algorithms that can accurately find cancerous regions are very valuable for the interpretation of VLE data. In this study, we propose a fully-automatic multi-step Computer-Aided Detection (CAD) algorithm that optimally leverages the effectiveness of deep learning strategies by encoding the principal dimension in VLE data. Additionally, we show that combining the encoded dimensions with conventional machine learning techniques further improves results while maintaining interpretability. Furthermore, we train and validate our algorithm on a new histopathologically validated set of in-vivo VLE snapshots. Additionally, an independent test set is used to assess the performance of the model. Finally, we compare the performance of our algorithm against previous state-of-the-art systems. With the encoded principal dimension, we obtain an Area Under the Curve (AUC) and F1 score of 0.93 and 87.4% on the test set respectively. We show this is a significant improvement compared to the state-of-the-art of 0.89 and 83.1%, respectively, thereby demonstrating the effectiveness of our approach.
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Gulati S, Emmanuel A, Patel M, Williams S, Haji A, Hayee B, Neumann H. Artificial intelligence in luminal endoscopy. Ther Adv Gastrointest Endosc 2020; 13:2631774520935220. [PMID: 32637935 PMCID: PMC7315657 DOI: 10.1177/2631774520935220] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 05/22/2020] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence is a strong focus of interest for global health development. Diagnostic endoscopy is an attractive substrate for artificial intelligence with a real potential to improve patient care through standardisation of endoscopic diagnosis and to serve as an adjunct to enhanced imaging diagnosis. The possibility to amass large data to refine algorithms makes adoption of artificial intelligence into global practice a potential reality. Initial studies in luminal endoscopy involve machine learning and are retrospective. Improvement in diagnostic performance is appreciable through the adoption of deep learning. Research foci in the upper gastrointestinal tract include the diagnosis of neoplasia, including Barrett's, squamous cell and gastric where prospective and real-time artificial intelligence studies have been completed demonstrating a benefit of artificial intelligence-augmented endoscopy. Deep learning applied to small bowel capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. Prospective evaluation including the first randomised trial has been performed in the colon, demonstrating improved polyp and adenoma detection rates; however, these appear to be relevant to small polyps. There are potential additional roles of artificial intelligence relevant to improving the quality of endoscopic examinations, training and triaging of referrals. Further large-scale, multicentre and cross-platform validation studies are required for the robust incorporation of artificial intelligence-augmented diagnostic luminal endoscopy into our routine clinical practice.
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Affiliation(s)
- Shraddha Gulati
- King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Andrew Emmanuel
- King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Mehul Patel
- King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Sophie Williams
- King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Amyn Haji
- King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Bu’Hussain Hayee
- King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Helmut Neumann
- Department of Interdisciplinary Endoscopy, University Hospital Mainz, 55131 Mainz, Germany
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20
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Zeng Y, Nandy S, Rao B, Li S, Hagemann AR, Kuroki LK, McCourt C, Mutch DG, Powell MA, Hagemann IS, Zhu Q. Histogram analysis of en face scattering coefficient map predicts malignancy in human ovarian tissue. JOURNAL OF BIOPHOTONICS 2019; 12:e201900115. [PMID: 31304678 PMCID: PMC7982142 DOI: 10.1002/jbio.201900115] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Revised: 06/22/2019] [Accepted: 07/11/2019] [Indexed: 05/18/2023]
Abstract
Ovarian cancer is a heterogeneous disease at the molecular and histologic level. Optical coherence tomography (OCT) is able to map ovarian tissue optical properties and heterogeneity, which has been proposed as a feature to aid in diagnosis of ovarian cancer. In this manuscript, depth-resolved en face scattering maps of malignant ovaries, benign ovaries, and benign fallopian tubes obtained from 20 patients are provided to visualize the heterogeneity of ovarian tissues. Six features are extracted from histograms of scattering maps. All features are able to statistically distinguish benign from malignant ovaries. Two prediction models were constructed based on these features: a logistic regression model (LR) and a support vector machine (SVM). The optimal set of features is mean scattering coefficient and scattering map entropy. The LR achieved a sensitivity and specificity of 97.0% and 97.8%, and SVM demonstrated a sensitivity and specificity of 99.6% and 96.4%. Our initial results demonstrate the feasibility of using OCT as an "optical biopsy tool" for detecting the microscopic scattering changes associated with neoplasia in human ovarian tissue.
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Affiliation(s)
- Yifeng Zeng
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri
| | - Sreyankar Nandy
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri
| | - Bin Rao
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri
| | - Shuying Li
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri
| | - Andrea R. Hagemann
- Department of Obstetrics & Gynecology, Washington University School of Medicine, St. Louis, Missouri
| | - Lindsay K. Kuroki
- Department of Obstetrics & Gynecology, Washington University School of Medicine, St. Louis, Missouri
| | - Carolyn McCourt
- Department of Obstetrics & Gynecology, Washington University School of Medicine, St. Louis, Missouri
| | - David G. Mutch
- Department of Obstetrics & Gynecology, Washington University School of Medicine, St. Louis, Missouri
| | - Matthew A. Powell
- Department of Obstetrics & Gynecology, Washington University School of Medicine, St. Louis, Missouri
| | - Ian S. Hagemann
- Department of Pathology & Immunology, Washington University School of Medicine, St. Louis, Missouri
- Department of Obstetrics & Gynecology, Washington University School of Medicine, St. Louis, Missouri
| | - Quing Zhu
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri
- Correspondence Dr. Quing Zhu, Department of Biomedical Engineering, Washington University, St. Louis, MO 63110.
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21
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Soh YSA, Lee YY, Gotoda T, Sharma P, Ho KY. Challenges to diagnostic standardization of Barrett's esophagus in Asia. Dig Endosc 2019; 31:609-618. [PMID: 30892742 DOI: 10.1111/den.13402] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 03/13/2019] [Indexed: 12/14/2022]
Abstract
Barrett's esophagus (BE), a premalignant condition of the lower esophagus, is increasingly prevalent in Asia. However, endoscopic and histopathological criteria vary widely between studies across Asia, making it challenging to assess comparability between geographical regions. Furthermore, guidelines from various societies worldwide provide differing viewpoints and definitions, leading to diagnostic challenges that affect prognostication of the condition. In this review, the authors discuss the controversies surrounding the diagnosis of BE, particularly in Asia. Differences between guidelines worldwide are summarized with further discussion regarding various classifications of BE used, different definitions of gastroesophageal junction used across geographical regions and the clinical implications of intestinal metaplasia in the setting of BE. Although many guidelines recommend the Seattle protocol as the preferred approach regarding dysplasia surveillance in BE, some limitations exist, leading to poor adherence. Newer technologies, such as acetic acid-enhanced magnification endoscopy, narrow band imaging, Raman spectroscopy, molecular approaches and the use of artificial intelligence appear promising in addressing these problems, but further studies are required before implementation into routine clinical practice. The Asian Barrett's Consortium also outlines its ongoing plans to tackle the challenge of standardizing the diagnosis of BE in Asia.
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Affiliation(s)
- Yu Sen Alex Soh
- Department of Gastroenterology and Hepatology, National University Hospital, Singapore.,Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Yeong Yeh Lee
- School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Takuji Gotoda
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo, Japan
| | - Prateek Sharma
- Gastroenterology and Hepatology, Veterans Affairs Medical Center, Kansas City, USA.,Gastroenterology, University of Kansas, School of Medicine, Kansas City, USA
| | - Khek-Yu Ho
- Department of Gastroenterology and Hepatology, National University Hospital, Singapore.,Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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Ensemble of Deep Convolutional Neural Networks for Classification of Early Barrett’s Neoplasia Using Volumetric Laser Endomicroscopy. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9112183] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Barrett’s esopaghagus (BE) is a known precursor of esophageal adenocarcinoma (EAC). Patients with BE undergo regular surveillance to early detect stages of EAC. Volumetric laser endomicroscopy (VLE) is a novel technology incorporating a second-generation form of optical coherence tomography and is capable of imaging the inner tissue layers of the esophagus over a 6 cm length scan. However, interpretation of full VLE scans is still a challenge for human observers. In this work, we train an ensemble of deep convolutional neural networks to detect neoplasia in 45 BE patients, using a dataset of images acquired with VLE in a multi-center study. We achieve an area under the receiver operating characteristic curve (AUC) of 0.96 on the unseen test dataset and we compare our results with previous work done with VLE analysis, where only AUC of 0.90 was achieved via cross-validation on 18 BE patients. Our method for detecting neoplasia in BE patients facilitates future advances on patient treatment and provides clinicians with new assisting solutions to process and better understand VLE data.
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23
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Early esophageal adenocarcinoma detection using deep learning methods. Int J Comput Assist Radiol Surg 2019; 14:611-621. [PMID: 30666547 PMCID: PMC6420905 DOI: 10.1007/s11548-019-01914-4] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Accepted: 01/07/2019] [Indexed: 02/08/2023]
Abstract
Purpose This study aims to adapt and evaluate the performance of different state-of-the-art deep learning object detection methods to automatically identify esophageal adenocarcinoma (EAC) regions from high-definition white light endoscopy (HD-WLE) images. Method Several state-of-the-art object detection methods using Convolutional Neural Networks (CNNs) were adapted to automatically detect abnormal regions in the esophagus HD-WLE images, utilizing VGG’16 as the backbone architecture for feature extraction. Those methods are Regional-based Convolutional Neural Network (R-CNN), Fast R-CNN, Faster R-CNN and Single-Shot Multibox Detector (SSD). For the evaluation of the different methods, 100 images from 39 patients that have been manually annotated by five experienced clinicians as ground truth have been tested. Results Experimental results illustrate that the SSD and Faster R-CNN networks show promising results, and the SSD outperforms other methods achieving a sensitivity of 0.96, specificity of 0.92 and F-measure of 0.94. Additionally, the Average Recall Rate of the Faster R-CNN in locating the EAC region accurately is 0.83. Conclusion In this paper, recent deep learning object detection methods are adapted to detect esophageal abnormalities automatically. The evaluation of the methods proved its ability to locate abnormal regions in the esophagus from endoscopic images. The automatic detection is a crucial step that may help early detection and treatment of EAC and also can improve automatic tumor segmentation to monitor its growth and treatment outcome.
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24
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Computer-Aided Analysis of Gland-Like Subsurface Hyposcattering Structures in Barrett’s Esophagus Using Optical Coherence Tomography. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8122420] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
(1) Background: Barrett’s esophagus (BE) is a complication of chronic gastroesophageal reflux disease and is a precursor to esophageal adenocarcinoma. The clinical implication of subsurface glandular structures of Barrett’s esophagus is not well understood. Optical coherence tomography (OCT), also known as volumetric laser endomicroscopy (VLE), can assess subsurface glandular structures, which appear as subsurface hyposcattering structures (SHSs). The aim of this study is to develop a computer-aided algorithm and apply it to investigate the characteristics of SHSs in BE using clinical VLE data; (2) Methods: SHSs were identified with an initial detection followed by machine learning. Comprehensive SHS characteristics including the number, volume, depth, size and shape were quantified. Clinical VLE datasets collected from 35 patients with a history of dysplasia undergoing BE surveillance were analyzed to study the general SHS distribution and characteristics in BE. A subset of radiofrequency ablation (RFA) patient data were further analyzed to investigate the pre-RFA SHS characteristics and post-RFA treatment response; (3) Results: SHSs in the BE region were significantly shallower, more vertical, less eccentric, and more regular, as compared with squamous SHSs. SHSs in the BE region which became neosquamous epithelium after RFA were shallower than those in the regions that remained BE. Pre-ablation squamous SHSs with higher eccentricity correlated strongly with larger reduction of post-ablation BE length for less elderly patients; (4) Conclusions: The computer algorithm is potentially a valuable tool for studying the roles of SHSs in BE.
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25
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Gan M, Wang C, Yang T, Yang N, Zhang M, Yuan W, Li X, Wang L. Robust layer segmentation of esophageal OCT images based on graph search using edge-enhanced weights. BIOMEDICAL OPTICS EXPRESS 2018; 9:4481-4495. [PMID: 30615715 PMCID: PMC6157790 DOI: 10.1364/boe.9.004481] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 08/17/2018] [Accepted: 08/20/2018] [Indexed: 05/18/2023]
Abstract
Automatic segmentation of esophageal layers in OCT images is crucial for studying esophageal diseases and computer-assisted diagnosis. This work aims to improve the current techniques to increase the accuracy and robustness for esophageal OCT image segmentation. A two-step edge-enhanced graph search (EEGS) framework is proposed in this study. Firstly, a preprocessing scheme is applied to suppress speckle noise and remove the disturbance in the esophageal structure. Secondly, the image is formulated into a graph and layer boundaries are located by graph search. In this process, we propose an edge-enhanced weight matrix for the graph by combining the vertical gradients with a Canny edge map. Experiments on esophageal OCT images from guinea pigs demonstrate that the EEGS framework is more robust and more accurate than the current segmentation method. It can be potentially useful for the early detection of esophageal diseases.
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Affiliation(s)
- Meng Gan
- Department of Electronic and Information Engineering, Soochow University, Suzhou 215006,
China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163,
China
| | - Cong Wang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163,
China
| | - Ting Yang
- Department of Electronic and Information Engineering, Soochow University, Suzhou 215006,
China
| | - Na Yang
- Department of Electronic and Information Engineering, Soochow University, Suzhou 215006,
China
| | - Miao Zhang
- Department of Electronic and Information Engineering, Soochow University, Suzhou 215006,
China
| | - Wu Yuan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205,
USA
| | - Xingde Li
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205,
USA
| | - Lirong Wang
- Department of Electronic and Information Engineering, Soochow University, Suzhou 215006,
China
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26
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Wang B, Wang HW, Guo H, Anderson E, Tang Q, Wu T, Falola R, Smith T, Andrews PM, Chen Y. Optical coherence tomography and computer-aided diagnosis of a murine model of chronic kidney disease. JOURNAL OF BIOMEDICAL OPTICS 2017; 22:1-11. [PMID: 29197178 PMCID: PMC5745648 DOI: 10.1117/1.jbo.22.12.121706] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Accepted: 11/13/2017] [Indexed: 05/02/2023]
Abstract
Chronic kidney disease (CKD) is characterized by a progressive loss of renal function over time. Histopathological analysis of the condition of glomeruli and the proximal convolutional tubules over time can provide valuable insights into the progression of CKD. Optical coherence tomography (OCT) is a technology that can analyze the microscopic structures of a kidney in a nondestructive manner. Recently, we have shown that OCT can provide real-time imaging of kidney microstructures in vivo without administering exogenous contrast agents. A murine model of CKD induced by intravenous Adriamycin (ADR) injection is evaluated by OCT. OCT images of the rat kidneys have been captured every week up to eight weeks. Tubular diameter and hypertrophic tubule population of the kidneys at multiple time points after ADR injection have been evaluated through a fully automated computer-vision system. Results revealed that mean tubular diameter and hypertrophic tubule population increase with time in post-ADR injection period. The results suggest that OCT images of the kidney contain abundant information about kidney histopathology. Fully automated computer-aided diagnosis based on OCT has the potential for clinical evaluation of CKD conditions.
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Affiliation(s)
- Bohan Wang
- University of Maryland, Department of Electrical and Computer Engineering, College Park, Maryland, United States
| | - Hsing-Wen Wang
- University of Maryland, Fischell Department of Bioengineering, College Park, Maryland, United States
| | - Hengchang Guo
- University of Maryland, Fischell Department of Bioengineering, College Park, Maryland, United States
| | - Erik Anderson
- Georgetown University Medical Center, Department of Biochemistry and Molecular and Cellular Biology, Washington, DC, United States
| | - Qinggong Tang
- University of Maryland, Fischell Department of Bioengineering, College Park, Maryland, United States
| | - Tongtong Wu
- University of Rochester, Department of Biostatistics and Computational Biology, Rochester, New York, United States
| | - Reuben Falola
- Georgetown University Medical Center, Department of Biochemistry and Molecular and Cellular Biology, Washington, DC, United States
| | - Tikina Smith
- University of Maryland, Central Animal Resources Facility, College Park, Maryland, United States
| | - Peter M. Andrews
- Georgetown University Medical Center, Department of Biochemistry and Molecular and Cellular Biology, Washington, DC, United States
| | - Yu Chen
- University of Maryland, Department of Electrical and Computer Engineering, College Park, Maryland, United States
- University of Maryland, Fischell Department of Bioengineering, College Park, Maryland, United States
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Computer-aided detection of early Barrett's neoplasia using volumetric laser endomicroscopy. Gastrointest Endosc 2017; 86:839-846. [PMID: 28322771 DOI: 10.1016/j.gie.2017.03.011] [Citation(s) in RCA: 97] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Accepted: 03/05/2017] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS Volumetric laser endomicroscopy (VLE) is an advanced imaging system that provides a near-microscopic resolution scan of the esophageal wall layers up to 3-mm deep. VLE has the potential to improve detection of early neoplasia in Barrett's esophagus (BE). However, interpretation of VLE images is complex because of the large amount of data that need to be interpreted in real time. The aim of this study was to investigate the feasibility of a computer algorithm to identify early BE neoplasia on ex vivo VLE images. METHODS We used 60 VLE images from a database of high-quality ex vivo VLE-histology correlations, obtained from BE patients ± neoplasia (30 nondysplastic BE [NDBE] and 30 high-grade dysplasia/early adenocarcinoma images). VLE features from a recently developed clinical VLE prediction score for BE neoplasia served as input for the algorithm: (1) higher VLE surface than subsurface signal and (2) lack of layering. With this input, novel clinically inspired algorithm features were developed, based on signal intensity statistics and grayscale correlations. For comparison, generic image analysis methods were examined for their performance to detect neoplasia. For classification of the images in the NDBE or neoplastic group, several machine learning methods were evaluated. Leave-1-out cross-validation was used for algorithm validation. RESULTS Three novel clinically inspired algorithm features were developed. The feature "layering and signal decay statistics" showed the optimal performance compared with the other clinically features ("layering" and "signal intensity distribution") and generic image analyses methods, with an area under the receiver operating characteristic curve (AUC) of .95. Corresponding sensitivity and specificity were 90% and 93%, respectively. In addition, the algorithm showed a better performance than the clinical VLE prediction score (AUC .81). CONCLUSIONS This is the first study in which a computer algorithm for BE neoplasia was developed based on VLE images with direct histologic correlates. The algorithm showed good performance to detect BE neoplasia in ex vivo VLE images compared with the performance of a recently developed clinical VLE prediction score. This study suggests that an automatic detection algorithm has the potential to assist endoscopists in detecting early neoplasia on VLE. Future studies on in vivo VLE scans are needed to further validate the algorithm.
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Swager AF, Faber DJ, de Bruin DM, Weusten BL, Meijer SL, Bergman JJ, Curvers WL, van Leeuwen TG. Quantitative attenuation analysis for identification of early Barrett's neoplasia in volumetric laser endomicroscopy. JOURNAL OF BIOMEDICAL OPTICS 2017; 22:86001. [PMID: 28777838 DOI: 10.1117/1.jbo.22.8.086001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Accepted: 07/17/2017] [Indexed: 06/07/2023]
Abstract
Early neoplasia in Barrett’s esophagus (BE) is difficult to detect. Volumetric laser endomicroscopy (VLE) incorporates optical coherence tomography, providing a circumferential scan of the esophageal wall layers. The attenuation coefficient (μVLE) quantifies decay of detected backscattered light versus depth, and could potentially improve BE neoplasia detection. The aim is to investigate feasibility of μVLE for identification of early BE neoplasia. In vivo and ex vivo VLE scans with histological correlation from BE patients ± neoplasia were used. Quantification by μVLE was performed manually on areas of interest (AoIs) to differentiate neoplasia from nondysplastic (ND)BE. From ex vivo VLE scans from 16 patients (13 with neoplasia), 68 AoIs were analyzed. Median μVLE values (mm−1) were 3.7 [2.1 to 4.4 interquartile range (IQR)] for NDBE and 4.0 (2.5 to 4.9 IQR) for neoplasia, not statistically different (p=0.82). Fourteen in vivo scans were used: nine from neoplastic and five from NDBE patients. Median μVLE values were 1.8 (1.5 to 2.6 IQR) for NDBE and 2.1 (1.9 to 2.6 IQR) for neoplasia, with no statistically significant difference (p=0.37). In conclusion, there was no significant difference in μVLE values in VLE scans from early neoplasia versus NDBE. Future studies with a larger sample size should explore other quantitative methods for detection of neoplasia during BE surveillance.
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Affiliation(s)
- Anne-Fre Swager
- , Department of Gastroenterology and Hepatology, Academic Medical Center, Amsterdam
| | - Dirk J Faber
- , Department of Biomedical Engineering and Physics, Academic Medical Center, Amsterdam
| | - Daniel M de Bruin
- , Department of Biomedical Engineering and Physics, Academic Medical Center, Amsterdam
| | - Bas L Weusten
- , Department of Gastroenterology and Hepatology, Academic Medical Center, Amsterdam
| | - Sybren L Meijer
- , Department of Pathology, Academic Medical Center, Amsterdam
| | - Jacques J Bergman
- , Department of Gastroenterology and Hepatology, Academic Medical Center, Amsterdam
| | | | - Ton G van Leeuwen
- , Department of Biomedical Engineering and Physics, Academic Medical Center, Amsterdam
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Jiang Y, Gong Y, Rubenstein JH, Wang TD, Seibel EJ. Toward real-time quantification of fluorescence molecular probes using target/background ratio for guiding biopsy and endoscopic therapy of esophageal neoplasia. J Med Imaging (Bellingham) 2017; 4:024502. [PMID: 28560244 DOI: 10.1117/1.jmi.4.2.024502] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 04/24/2017] [Indexed: 12/20/2022] Open
Abstract
Multimodal endoscopy using fluorescence molecular probes is a promising method of surveying the entire esophagus to detect cancer progression. Using the fluorescence ratio of a target compared to a surrounding background, a quantitative value is diagnostic for progression from Barrett's esophagus to high-grade dysplasia (HGD) and esophageal adenocarcinoma (EAC). However, current quantification of fluorescent images is done only after the endoscopic procedure. We developed a Chan-Vese-based algorithm to segment fluorescence targets, and subsequent morphological operations to generate background, thus calculating target/background (T/B) ratios, potentially to provide real-time guidance for biopsy and endoscopic therapy. With an initial processing speed of 2 fps and by calculating the T/B ratio for each frame, our method provides quasireal-time quantification of the molecular probe labeling to the endoscopist. Furthermore, an automatic computer-aided diagnosis algorithm can be applied to the recorded endoscopic video, and the overall T/B ratio is calculated for each patient. The receiver operating characteristic curve was employed to determine the threshold for classification of HGD/EAC using leave-one-out cross-validation. With 92% sensitivity and 75% specificity to classify HGD/EAC, our automatic algorithm shows promising results for a surveillance procedure to help manage esophageal cancer and other cancers inspected by endoscopy.
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Affiliation(s)
- Yang Jiang
- University of Washington, Department of Bioengineering, Human Photonics Lab, Seattle, Washington, United States
| | - Yuanzheng Gong
- University of Washington, Department of Mechanical Engineering, Human Photonics Lab, Seattle, Washington, United States
| | - Joel H Rubenstein
- University of Michigan, Division of Gastroenterology, Department of Internal Medicine, Ann Arbor, Michigan, United States.,Veterans Affairs Center for Clinical Management Research, Ann Arbor, Michigan, United States
| | - Thomas D Wang
- University of Michigan, Division of Gastroenterology, Department of Internal Medicine, Ann Arbor, Michigan, United States
| | - Eric J Seibel
- University of Washington, Department of Mechanical Engineering, Human Photonics Lab, Seattle, Washington, United States
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30
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Takahashi R, Kajikawa Y. Computer-aided diagnosis: A survey with bibliometric analysis. Int J Med Inform 2017; 101:58-67. [DOI: 10.1016/j.ijmedinf.2017.02.004] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2016] [Revised: 01/28/2017] [Accepted: 02/04/2017] [Indexed: 12/18/2022]
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Yang F, Hamit M, Yan CB, Yao J, Kutluk A, Kong XM, Zhang SX. Feature Extraction and Classification on Esophageal X-Ray Images of Xinjiang Kazak Nationality. JOURNAL OF HEALTHCARE ENGINEERING 2017; 2017:4620732. [PMID: 29065605 PMCID: PMC5394892 DOI: 10.1155/2017/4620732] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Revised: 01/09/2017] [Accepted: 02/06/2017] [Indexed: 01/06/2023]
Abstract
Esophageal cancer is one of the fastest rising types of cancers in China. The Kazak nationality is the highest-risk group in Xinjiang. In this work, an effective computer-aided diagnostic system is developed to assist physicians in interpreting digital X-ray image features and improving the quality of diagnosis. The modules of the proposed system include image preprocessing, feature extraction, feature selection, image classification, and performance evaluation. 300 original esophageal X-ray images were resized to a region of interest and then enhanced by the median filter and histogram equalization method. 37 features from textural, frequency, and complexity domains were extracted. Both sequential forward selection and principal component analysis methods were employed to select the discriminative features for classification. Then, support vector machine and K-nearest neighbors were applied to classify the esophageal cancer images with respect to their specific types. The classification performance was evaluated in terms of the area under the receiver operating characteristic curve, accuracy, precision, and recall, respectively. Experimental results show that the classification performance of the proposed system outperforms the conventional visual inspection approaches in terms of diagnostic quality and processing time. Therefore, the proposed computer-aided diagnostic system is promising for the diagnostics of esophageal cancer.
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Affiliation(s)
- Fang Yang
- Department of Medical Engineering, The Affiliated Tumor Hospital, Xinjiang Medical University, Urumqi 830011, China
| | - Murat Hamit
- College of Medical Engineering Technology, Xinjiang Medical University, Urumqi 830011, China
| | - Chuan B. Yan
- College of Medical Engineering Technology, Xinjiang Medical University, Urumqi 830011, China
| | - Juan Yao
- Department of Radiology, The First Affiliated Hospital, Xinjiang Medical University, Urumqi 830054, China
| | - Abdugheni Kutluk
- College of Medical Engineering Technology, Xinjiang Medical University, Urumqi 830011, China
| | - Xi M. Kong
- College of Medical Engineering Technology, Xinjiang Medical University, Urumqi 830011, China
| | - Sui X. Zhang
- College of Medical Engineering Technology, Xinjiang Medical University, Urumqi 830011, China
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32
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Li Z, Tang Q, Jin L, Andrews PM, Chen Y. Monitoring kidney microanatomy changes during ischemia-reperfusion process using texture analysis of OCT images. IEEE PHOTONICS JOURNAL 2017; 9:4000110. [PMID: 29104734 PMCID: PMC5665408 DOI: 10.1109/jphot.2017.2669482] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
kidney ischemia-reperfusion (I/R) accounts for the majority of acute kidney injury cases, whose consequences are commonly encountered after kidney transplantation. Optical coherence tomography (OCT) has been applied to image changes in kidney microanatomy and microcirculation. In this paper, we demonstrate a quantitative method for monitoring kidney status during ischemia-reperfusion process using texture properties of OCT images. This approach employs skewness to measure the distribution of en face OCT image intensities at different depths, thus allowing differentiating ischemia-reperfusion status of kidney. The skewness analysis based on quantitative intensity shows promise for monitoring kidney status during ischemia-reperfusion, and the potential for evaluating the viability of transplant kidney.
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Affiliation(s)
- Zhifang Li
- College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, 350007, China
- Fischell Department of Bioengineering, University of Maryland, College Park, MD, 20742
| | - Qinggong Tang
- Fischell Department of Bioengineering, University of Maryland, College Park, MD, 20742
| | - Lili Jin
- Fischell Department of Bioengineering, University of Maryland, College Park, MD, 20742
| | | | - Yu Chen
- College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, 350007, China
- Fischell Department of Bioengineering, University of Maryland, College Park, MD, 20742
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33
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Wan S, Lee HC, Huang X, Xu T, Xu T, Zeng X, Zhang Z, Sheikine Y, Connolly JL, Fujimoto JG, Zhou C. Integrated local binary pattern texture features for classification of breast tissue imaged by optical coherence microscopy. Med Image Anal 2017; 38:104-116. [PMID: 28327449 DOI: 10.1016/j.media.2017.03.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Revised: 03/06/2017] [Accepted: 03/07/2017] [Indexed: 12/20/2022]
Abstract
This paper proposes a texture analysis technique that can effectively classify different types of human breast tissue imaged by Optical Coherence Microscopy (OCM). OCM is an emerging imaging modality for rapid tissue screening and has the potential to provide high resolution microscopic images that approach those of histology. OCM images, acquired without tissue staining, however, pose unique challenges to image analysis and pattern classification. We examined multiple types of texture features and found Local Binary Pattern (LBP) features to perform better in classifying tissues imaged by OCM. In order to improve classification accuracy, we propose novel variants of LBP features, namely average LBP (ALBP) and block based LBP (BLBP). Compared with the classic LBP feature, ALBP and BLBP features provide an enhanced encoding of the texture structure in a local neighborhood by looking at intensity differences among neighboring pixels and among certain blocks of pixels in the neighborhood. Fourty-six freshly excised human breast tissue samples, including 27 benign (e.g. fibroadenoma, fibrocystic disease and usual ductal hyperplasia) and 19 breast carcinoma (e.g. invasive ductal carcinoma, ductal carcinoma in situ and lobular carcinoma in situ) were imaged with large field OCM with an imaging area of 10 × 10 mm2 (10, 000 × 10, 000 pixels) for each sample. Corresponding H&E histology was obtained for each sample and used to provide ground truth diagnosis. 4310 small OCM image blocks (500 × 500 pixels) each paired with corresponding H&E histology was extracted from large-field OCM images and labeled with one of the five different classes: adipose tissue (n = 347), fibrous stroma (n = 2,065), breast lobules (n = 199), carcinomas (pooled from all sub-types, n = 1,127), and background (regions outside of the specimens, n = 572). Our experiments show that by integrating a selected set of LBP and the two new variant (ALBP and BLBP) features at multiple scales, the classification accuracy increased from 81.7% (using LBP features alone) to 93.8% using a neural network classifier. The integrated feature was also used to classify large-field OCM images for tumor detection. A receiver operating characteristic (ROC) curve was obtained with an area under the curve value of 0.959. A sensitivity level of 100% and specificity level of 85.2% was achieved to differentiate benign from malignant samples. Several other experiments also demonstrate the complementary nature of LBP and the two variants (ALBP and BLBP features) and the significance of integrating these texture features for classification. Using features from multiple scales and performing feature selection are also effective mechanisms to improve accuracy while maintaining computational efficiency.
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Affiliation(s)
- Sunhua Wan
- Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Hsiang-Chieh Lee
- Department of Electrical Engineering and Computer Science and Research Laboratory of Electronics, MIT, Cambridge, MA 02139, USA
| | - Xiaolei Huang
- Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015, USA.
| | - Ting Xu
- Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Tao Xu
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Xianxu Zeng
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, USA; The Third Affiliated Hospital of Zhengzhou University, Henan, China
| | - Zhan Zhang
- The Third Affiliated Hospital of Zhengzhou University, Henan, China
| | - Yuri Sheikine
- Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02115, USA
| | - James L Connolly
- Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02115, USA
| | - James G Fujimoto
- Department of Electrical Engineering and Computer Science and Research Laboratory of Electronics, MIT, Cambridge, MA 02139, USA
| | - Chao Zhou
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, USA; Center for Photonics and Nanoelectronics, Lehigh University, Bethlehem, PA 18015, USA; Bioengineering Program, Lehigh University, Bethlehem, PA 18015, USA.
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Hou F, Yu Y, Liang Y. Automatic identification of parathyroid in optical coherence tomography images. Lasers Surg Med 2017; 49:305-311. [PMID: 28129441 DOI: 10.1002/lsm.22622] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/23/2016] [Indexed: 01/15/2023]
Abstract
BACKGROUND AND OBJECTIVE The identification and preservation of parathyroid is a major problem in thyroid surgery. In order to solve this problem, optical coherence tomography was involved as a real-time, non-invasive high-resolution imaging technique. This study demonstrated an effective and fast method to distinguish parathyroid tissue from thyroid, lymph node, and adipose tissue in their ex vivo optical coherence tomography (OCT) images automatically. METHODS OCT images were obtained from parathyroid, thyroid, lymph node, and adipose tissue, respectively. A classification and an identification system based on texture features analysis and back propagation artificial neural network (BP-ANN) were established to classify the four types of tissue and identify each of the four types automatically. RESULTS A total of 248 OCT images were taken from 16 patients undergoing thyroidectomy. The accuracy of classification for parathyroid, thyroid, lymph node, and adipose were 99.21, 98.43, 97.65, and 98.43%, respectively. CONCLUSION The proposed automatic identification method is capable of distinguishing among parathyroid, thyroid, lymph, and adipose automatically and effectively. Compared with the identification results of human, it has a better accuracy and reliability. For identifying parathyroid from the other entities, it has a satisfying performance. Lasers Surg. Med. 49:305-311, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Fang Hou
- Institute of Modern Optics, Nankai University, Key Laboratory of Optical Information Science and Technology, Ministry of Education, Tianjin, 300071, China
| | - Yang Yu
- Department of Thyroid and Neck Tumor, Tianjin Medical University Cancer Institute & Hospital, Oncology Key Laboratory of Cancer Prevention & Therapy, Tianjin, 300060, China
| | - Yanmei Liang
- Institute of Modern Optics, Nankai University, Key Laboratory of Optical Information Science and Technology, Ministry of Education, Tianjin, 300071, China
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Marvdashti T, Duan L, Aasi SZ, Tang JY, Ellerbee Bowden AK. Classification of basal cell carcinoma in human skin using machine learning and quantitative features captured by polarization sensitive optical coherence tomography. BIOMEDICAL OPTICS EXPRESS 2016; 7:3721-3735. [PMID: 27699133 PMCID: PMC5030045 DOI: 10.1364/boe.7.003721] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Revised: 08/07/2016] [Accepted: 08/08/2016] [Indexed: 05/08/2023]
Abstract
We report the first fully automated detection of basal cell carcinoma (BCC), the most commonly occurring type of skin cancer, in human skin using polarization-sensitive optical coherence tomography (PS-OCT). Our proposed automated procedure entails building a machine-learning based classifier by extracting image features from the two complementary image contrasts offered by PS-OCT, intensity and phase retardation (PR), and selecting a subset of features that yields a classifier with the highest accuracy. Our classifier achieved 95.4% sensitivity and specificity, validated by leave-one-patient-out cross validation (LOPOCV), in detecting BCC in human skin samples collected from 42 patients. Moreover, we show the superiority of our classifier over the best possible classifier based on features extracted from intensity-only data, which demonstrates the significance of PR data in detecting BCC.
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Affiliation(s)
- Tahereh Marvdashti
- E. L. Ginzton Laboratory and Department of Electrical Engineering, Stanford University, Stanford, CA 94305,
USA
| | - Lian Duan
- E. L. Ginzton Laboratory and Department of Electrical Engineering, Stanford University, Stanford, CA 94305,
USA
| | - Sumaira Z. Aasi
- Department of Dermatology, Stanford University, Stanford, CA 94305,
USA
| | - Jean Y. Tang
- Department of Dermatology, Stanford University, Stanford, CA 94305,
USA
| | - Audrey K. Ellerbee Bowden
- E. L. Ginzton Laboratory and Department of Electrical Engineering, Stanford University, Stanford, CA 94305,
USA
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36
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Dysplasia discrimination in intestinal-type neoplasia of the esophagus and colon via digital image analysis. Virchows Arch 2016; 469:405-15. [PMID: 27492044 DOI: 10.1007/s00428-016-1999-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2016] [Revised: 07/06/2016] [Accepted: 07/25/2016] [Indexed: 01/26/2023]
Abstract
Determining gastrointestinal tract dysplasia level is clinically important but can be difficult, and given this challenge, we investigated colonic and esophageal dysplastic progression using digital image analysis (IA). Whole slide images were obtained for colonic normal mucosa (NCM), hyperplastic polyps (HP), conventional tubular adenomas (TA), and adenomas with high-grade dysplasia (HGD), and esophageal intestinal metaplasia negative for dysplasia (IM), indefinite for dysplasia (IFD), low-grade dysplasia (LGD), and HGD. Characteristic nuclei were circumscribed, and parameters discriminating groups included nuclear circumference (μm), area (μm(2)), and 15 positive pixel count (PPC) algorithm IA measurements. In colon polyps and esophageal lesions, average nuclear area and circumference ranged 30-108.6 μm(2) and 27.5-48.9 μm, respectively. Differences for average nuclear area and circumference met statistical significance (p < 0.05) between diagnostic groups in the esophagus and colon, except for IM versus IFD nuclear area. Pixel intensity (brightness) separated lesions within both groups with statistical significance except for colonic TAs versus HPs and esophageal LGD versus IM. HGD nuclei in both groups demonstrated more pixel staining heterogeneity than other lesions. Hierarchical clustering and principal component analysis demonstrated that lesions with similar diagnoses tended to cluster together on a low- to high-grade spectrum. Our results confirm that quantitative IA is an effective adjunct reflecting dysplasia in colon polyps and Barrett esophagus lesions. Nuclear area, circumference, and PPC algorithm findings distinguished lesions in a statistically significant manner. This suggests utility for future studies on similar methods, which may provide an adjunctive ancillary technique for pathologists and enhance patient care.
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Ughi GJ, Gora MJ, Swager AF, Soomro A, Grant C, Tiernan A, Rosenberg M, Sauk JS, Nishioka NS, Tearney GJ. Automated segmentation and characterization of esophageal wall in vivo by tethered capsule optical coherence tomography endomicroscopy. BIOMEDICAL OPTICS EXPRESS 2016; 7:409-19. [PMID: 26977350 PMCID: PMC4771459 DOI: 10.1364/boe.7.000409] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2015] [Revised: 12/16/2015] [Accepted: 12/16/2015] [Indexed: 05/18/2023]
Abstract
Optical coherence tomography (OCT) is an optical diagnostic modality that can acquire cross-sectional images of the microscopic structure of the esophagus, including Barrett's esophagus (BE) and associated dysplasia. We developed a swallowable tethered capsule OCT endomicroscopy (TCE) device that acquires high-resolution images of entire gastrointestinal (GI) tract luminal organs. This device has a potential to become a screening method that identifies patients with an abnormal esophagus that should be further referred for upper endoscopy. Currently, the characterization of the OCT-TCE esophageal wall data set is performed manually, which is time-consuming and inefficient. Additionally, since the capsule optics optimally focus light approximately 500 µm outside the capsule wall and the best quality images are obtained when the tissue is in full contact with the capsule, it is crucial to provide feedback for the operator about tissue contact during the imaging procedure. In this study, we developed a fully automated algorithm for the segmentation of in vivo OCT-TCE data sets and characterization of the esophageal wall. The algorithm provides a two-dimensional representation of both the contact map from the data collected in human clinical studies as well as a tissue map depicting areas of BE with or without dysplasia. Results suggest that these techniques can potentially improve the current TCE data acquisition procedure and provide an efficient characterization of the diseased esophageal wall.
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Affiliation(s)
- Giovanni J. Ughi
- Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Michalina J. Gora
- Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- ICube, CNRS, Strasbourg University, France
| | - Anne-Fré Swager
- Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA, USA
| | - Amna Soomro
- Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA, USA
| | - Catriona Grant
- Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA, USA
| | - Aubrey Tiernan
- Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA, USA
| | - Mireille Rosenberg
- Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA, USA
| | | | - Norman S. Nishioka
- Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA
| | - Guillermo J. Tearney
- Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
- Harvard-MIT Division of Health Sciences Technology, Cambridge, MA, USA
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Sharma N, Srivastava S, Kern F, Xian W, Ming T, McKeon F, Ho KY. Endoscopic modalities for the diagnosis of Barrett's oesophagus. United European Gastroenterol J 2015; 4:733-740. [PMID: 28408990 DOI: 10.1177/2050640615619281] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Accepted: 11/02/2015] [Indexed: 12/22/2022] Open
Abstract
Barrett's oesophagus is a pre-malignant condition associated with the development of oesophageal adenocarcinoma. Currently white light endoscopy and biopsy is the mainstay diagnostic tool. Yet this approach is troubled by issues related to cumbersome biopsy sampling, biopsy sampling errors and cost. Therefore in order to overcome such adversity, there needs to be evolutionary advancement in terms of diagnosis, which should address these concerns and ideally enhance risk stratification in order to provide timely management in real time. This review highlights the current endoscopic tools aimed to enhance the diagnosis of Barrett's oesophagus and its subsequent progression.
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Affiliation(s)
| | | | | | - Wa Xian
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA, MultiClonal Therapeutics, Inc., Farmington, CT, USA
| | - Teh Ming
- National University Hospital, Singapore
| | - Frank McKeon
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA, MultiClonal Therapeutics, Inc., Farmington, CT, USA
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Kang D, Wang A, Volgger V, Chen Z, Wong BJF. Spatiotemporal correlation of optical coherence tomography in-vivo images of rabbit airway for the diagnosis of edema. JOURNAL OF BIOMEDICAL OPTICS 2015; 20:076015. [PMID: 26222962 PMCID: PMC4518273 DOI: 10.1117/1.jbo.20.7.076015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2015] [Accepted: 06/30/2015] [Indexed: 03/28/2024]
Abstract
Detection of an early stage of subglottic edema is vital for airway management and prevention of stenosis, a life-threatening condition in critically ill neonates. As an observer for the task of diagnosing edema in vivo, we investigated spatiotemporal correlation (STC) of full-range optical coherence tomography (OCT) images acquired in the rabbit airway with experimentally simulated edema. Operating the STC observer on OCT images generates STC coefficients as test statistics for the statistical decision task. Resulting from this, the receiver operating characteristic (ROC) curves for the diagnosis of airway edema with full-range OCT in-vivo images were extracted and areas under ROC curves were calculated. These statistically quantified results demonstrated the potential clinical feasibility of the STC method as a means to identify early airway edema.
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Affiliation(s)
- DongYel Kang
- Hanbat National University, College of Engineering, School of Basic Sciences, 125 DogSeoDaeRo, YuSeong-Gu, Daejeon 305-719, Republic of Korea
| | - Alex Wang
- University of California Irvine, Beckman Laser Institute and Medical Clinic, Irvine, California 92617, United States
| | - Veronika Volgger
- Ludwig-Maximilians-University Munich, Department of Otolaryngology-Head and Neck Surgery, Marchioninistr. 15, Munich 81377, Germany
| | - Zhongping Chen
- University of California Irvine, Beckman Laser Institute and Medical Clinic, Irvine, California 92617, United States
- University of California Irvine, Department of Biomedical Engineering, Irvine, California 92617, United States
- University of California Irvine, Department of Otolaryngology-Head and Neck Surgery, Irvine, California 92617, United States
| | - Brian J. F. Wong
- University of California Irvine, Beckman Laser Institute and Medical Clinic, Irvine, California 92617, United States
- University of California Irvine, Department of Biomedical Engineering, Irvine, California 92617, United States
- University of California Irvine, Department of Otolaryngology-Head and Neck Surgery, Irvine, California 92617, United States
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Sommerey S, Ladurner R, Al Arabi N, Mortensen U, Hallfeldt K, Gallwas J. Backscattering intensity measurements in optical coherence tomography as a method to identify parathyroid glands. Lasers Surg Med 2015; 47:526-32. [PMID: 26032506 DOI: 10.1002/lsm.22367] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/08/2015] [Indexed: 11/11/2022]
Abstract
BACKGROUND AND OBJECTIVE Previous studies have shown that the use of optical coherence tomography (OCT) permits the differentiation between parathyroid tissue, thyroid tissue, lymph nodes and adipose tissue. We investigated the backscattering intensity profiles of OCT images in order to determine whether significant differences between these tissue types exist. METHODS Mean backscattering intensity profiles were obtained from OCT images of parathyroid glands, thyroid tissue, lymph nodes and adipose tissue. The profiles were analyzed employing Fisher's Linear Discriminant Analysis (LDA). The results were cross validated employing improved parameter estimation techniques. RESULTS Mean backscattering intensity profiles from 300 OCT images of 34 patients undergoing thyroid or parathyroid surgery were analyzed. The overall rate of correct classifications was 96.15%. The cross validation employing improved parameter estimation techniques yielded results identical to those derived from Fisher's LDA. CONCLUSION Besides the individual assessment of OCT images by interpreting morphological criteria, backscattering intensity measurements can reliably distinguish between different tissue entities.
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Affiliation(s)
- Sandra Sommerey
- Department of Surgery, Ludwig Maximilians University Munich, Innenstadt Medical Campus, Nussbaumstrasse 20, Munich, 80336, Germany
| | - Roland Ladurner
- Department of Surgery, Ludwig Maximilians University Munich, Innenstadt Medical Campus, Nussbaumstrasse 20, Munich, 80336, Germany
| | - Norah Al Arabi
- Department of Surgery, Ludwig Maximilians University Munich, Innenstadt Medical Campus, Nussbaumstrasse 20, Munich, 80336, Germany
| | - Uwe Mortensen
- Department of Psychology, Westfälische Wilhelms-University, Muenster, Germany
| | - Klaus Hallfeldt
- Department of Surgery, Ludwig Maximilians University Munich, Innenstadt Medical Campus, Nussbaumstrasse 20, Munich, 80336, Germany
| | - Julia Gallwas
- Department of Obstetrics and Gynecology, Ludwig Maximilians University Munich, Grosshadern Medical Campus, Marchioninistr. 15, Munich, 81377, Germany
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Abstract
Barrett's esophagus is the only known precursor that predisposes patients to the development of esophageal adenocarcinoma. The current recommended surveillance method is targeted biopsies of any abnormalities followed by random four-quadrant biopsies every 2 cm using standard white light endoscopy. Compliance with this and sampling error are two of the biggest problems. Several novel imaging technologies have been developed to aid the diagnosis of early neoplasia in Barrett's esophagus. There are emerging data that some of these new modalities can increase the yield of detecting dysplasia. This review will discuss some of the present available techniques and technologies including chromoendoscopy, narrow-band imaging, autofluorescence imaging, optical coherence tomography, confocal endomicroscopy and endocytoscopy. Based on the current evidence, these imaging modalities appear to be promising as adjunctive tools to white light endoscopy. A few of them, nevertheless, remain experimental due to expense, lack of expertise, generalizability as well as reproducibility of results.
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Affiliation(s)
- Rajvinder Singh
- University of Adelaide, Lyell McEwin Hospital, Gastroenterology and Surgery, Haydown Road, Elizabeth Vale, 5112 Australia
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42
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Robles LY, Singh S, Fisichella PM. Emerging enhanced imaging technologies of the esophagus: spectroscopy, confocal laser endomicroscopy, and optical coherence tomography. J Surg Res 2015; 195:502-14. [PMID: 25819772 DOI: 10.1016/j.jss.2015.02.045] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2014] [Revised: 02/04/2015] [Accepted: 02/18/2015] [Indexed: 02/06/2023]
Abstract
BACKGROUND Despite advances in diagnoses and therapy, esophageal adenocarcinoma remains a highly lethal neoplasm. Hence, a great interest has been placed in detecting early lesions and in the detection of Barrett esophagus (BE). Advanced imaging technologies of the esophagus have then been developed with the aim of improving biopsy sensitivity and detection of preplastic and neoplastic cells. The purpose of this article was to review emerging imaging technologies for esophageal pathology, spectroscopy, confocal laser endomicroscopy (CLE), and optical coherence tomography (OCT). METHODS We conducted a PubMed search using the search string "esophagus or esophageal or oesophageal or oesophagus" and "Barrett or esophageal neoplasm" and "spectroscopy or optical spectroscopy" and "confocal laser endomicroscopy" and "confocal microscopy" and "optical coherence tomography." The first and senior author separately reviewed all articles. Our search identified: 19 in vivo studies with spectroscopy that accounted for 1021 patients and 4 ex vivo studies; 14 clinical CLE in vivo studies that accounted for 941 patients and 1 ex vivo study with 13 patients; and 17 clinical OCT in vivo studies that accounted for 773 patients and 2 ex vivo studies. RESULTS Human studies using spectroscopy had a very high sensitivity and specificity for the detection of BE. CLE showed a high interobserver agreement in diagnosing esophageal pathology and an accuracy of predicting neoplasia. We also found several clinical studies that reported excellent diagnostic sensitivity and specificity for the detection of BE using OCT. CONCLUSIONS Advanced imaging technology for the detection of esophageal lesions is a promising field that aims to improve the detection of early esophageal lesions. Although advancing imaging techniques improve diagnostic sensitivities and specificities, their integration into diagnostic protocols has yet to be perfected.
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Affiliation(s)
| | - Satish Singh
- Division of Gastroenterology, Boston VA Healthcare System, Boston University, Boston, Massachusetts
| | - Piero Marco Fisichella
- Department of Surgery, Boston VA Healthcare System, Harvard Medical School, Boston, Massachusetts.
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43
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Tsai TH, Fujimoto JG, Mashimo H. Endoscopic Optical Coherence Tomography for Clinical Gastroenterology. Diagnostics (Basel) 2014; 4:57-93. [PMID: 26852678 PMCID: PMC4665545 DOI: 10.3390/diagnostics4020057] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2014] [Revised: 04/18/2014] [Accepted: 04/22/2014] [Indexed: 12/12/2022] Open
Abstract
Optical coherence tomography (OCT) is a real-time optical imaging technique that is similar in principle to ultrasonography, but employs light instead of sound waves and allows depth-resolved images with near-microscopic resolution. Endoscopic OCT allows the evaluation of broad-field and subsurface areas and can be used ancillary to standard endoscopy, narrow band imaging, chromoendoscopy, magnification endoscopy, and confocal endomicroscopy. This review article will provide an overview of the clinical utility of endoscopic OCT in the gastrointestinal tract and of recent achievements using state-of-the-art endoscopic 3D-OCT imaging systems.
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Affiliation(s)
- Tsung-Han Tsai
- Department of Electrical Engineering and Computer Science and Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - James G Fujimoto
- Department of Electrical Engineering and Computer Science and Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Hiroshi Mashimo
- Veterans Affairs Boston Healthcare System and Harvard Medical School, Boston, MA 02115, USA.
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44
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Wang HW, Chen Y. Clinical applications of optical coherence tomography in urology. INTRAVITAL 2014; 3:e28770. [PMID: 28243507 PMCID: PMC5312717 DOI: 10.4161/intv.28770] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2014] [Revised: 04/01/2014] [Accepted: 04/03/2014] [Indexed: 12/20/2022]
Abstract
Since optical coherence tomography (OCT) was first demonstrated in 1991, it has advanced significantly in technical aspects such as imaging speed and resolution, and has been clinically demonstrated in a diverse set of medical and surgical applications, including ophthalmology, cardiology, gastroenterology, dermatology, oncology, among others. This work reviews current clinical applications in urology, particularly in bladder, urether, and kidney. Clinical applications in bladder and urether mainly focus on cancer detection and staging based on tissue morphology, image contrast, and OCT backscattering. The application in kidney includes kidney cancer detection based on OCT backscattering attenuation and non-destructive evaluation of transplant kidney viability or acute tubular necrosis based on both tissue morphology from OCT images and function from Doppler OCT (DOCT) images. OCT holds the promise to positively impact the future clinical practices in urology.
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Affiliation(s)
- Hsing-Wen Wang
- Fischell Department of Bioengineering; University of Maryland; College Park, MD USA
| | - Yu Chen
- Fischell Department of Bioengineering; University of Maryland; College Park, MD USA
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45
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Montejo LD, Jia J, Kim HK, Netz UJ, Blaschke S, Müller GA, Hielscher AH. Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 1: feature extraction. JOURNAL OF BIOMEDICAL OPTICS 2013; 18:076001. [PMID: 23856915 PMCID: PMC3710917 DOI: 10.1117/1.jbo.18.7.076001] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
This is the first part of a two-part paper on the application of computer-aided diagnosis to diffuse optical tomography (DOT). An approach for extracting heuristic features from DOT images and a method for using these features to diagnose rheumatoid arthritis (RA) are presented. Feature extraction is the focus of Part 1, while the utility of five classification algorithms is evaluated in Part 2. The framework is validated on a set of 219 DOT images of proximal interphalangeal (PIP) joints. Overall, 594 features are extracted from the absorption and scattering images of each joint. Three major findings are deduced. First, DOT images of subjects with RA are statistically different (p<0.05) from images of subjects without RA for over 90% of the features investigated. Second, DOT images of subjects with RA that do not have detectable effusion, erosion, or synovitis (as determined by MRI and ultrasound) are statistically indistinguishable from DOT images of subjects with RA that do exhibit effusion, erosion, or synovitis. Thus, this subset of subjects may be diagnosed with RA from DOT images while they would go undetected by reviews of MRI or ultrasound images. Third, scattering coefficient images yield better one-dimensional classifiers. A total of three features yield a Youden index greater than 0.8. These findings suggest that DOT may be capable of distinguishing between PIP joints that are healthy and those affected by RA with or without effusion, erosion, or synovitis.
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Affiliation(s)
- Ludguier D. Montejo
- Columbia University, Department of Biomedical Engineering, New York, New York 10027
- Address all correspondence to: Ludguier D. Montejo and Andreas H. Hielscher, Columbia University, Department of Biomedical Engineering, 500 West 120th Street, ET 351 Mudd Bldg, MC8904, New York, New York 10027. Ludguier D. Montejo, Tel: 212-854-2320; Fax: 212-854-8725; E-mail: ; Andreas H. Hielscher, Tel: 212-854-5020; Fax: 212-854-8725; E-mail:
| | - Jingfei Jia
- Columbia University, Department of Biomedical Engineering, New York, New York 10027
| | - Hyun K. Kim
- Columbia University Medical Center, Department of Radiology, New York, New York 10032
| | - Uwe J. Netz
- Laser-und Medizin-Technologie GmbH Berlin, Berlin-Dahlem, 14195, Germany
- Charité-Universitätsmedizin Berlin, Department of Medical Physics and Laser Medicine, Berlin 10117, Germany
| | - Sabine Blaschke
- University Medical Center Göttingen, Department of Nephrology and Rheumatology, Göttingen 37075, Germany
| | - Gerhard A. Müller
- University Medical Center Göttingen, Department of Nephrology and Rheumatology, Göttingen 37075, Germany
| | - Andreas H. Hielscher
- Columbia University, Department of Biomedical Engineering, New York, New York 10027
- Columbia University Medical Center, Department of Radiology, New York, New York 10032
- Columbia University, Department of Electrical Engineering, New York, New York 10025
- Address all correspondence to: Ludguier D. Montejo and Andreas H. Hielscher, Columbia University, Department of Biomedical Engineering, 500 West 120th Street, ET 351 Mudd Bldg, MC8904, New York, New York 10027. Ludguier D. Montejo, Tel: 212-854-2320; Fax: 212-854-8725; E-mail: ; Andreas H. Hielscher, Tel: 212-854-5020; Fax: 212-854-8725; E-mail:
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Dhawan AP, D'Alessandro B, Fu X. Optical imaging modalities for biomedical applications. IEEE Rev Biomed Eng 2012; 3:69-92. [PMID: 22275202 DOI: 10.1109/rbme.2010.2081975] [Citation(s) in RCA: 64] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Optical photographic imaging is a well known imaging method that has been successfully translated into biomedical applications such as microscopy and endoscopy. Although several advanced medical imaging modalities are used today to acquire anatomical, physiological, metabolic, and functional information from the human body, optical imaging modalities including optical coherence tomography, confocal microscopy, multiphoton microscopy, multispectral endoscopy, and diffuse reflectance imaging have recently emerged with significant potential for non-invasive, portable, and cost-effective imaging for biomedical applications spanning tissue, cellular, and molecular levels. This paper reviews methods for modeling the propagation of light photons in a biological medium, as well as optical imaging from organ to cellular levels using visible and near-infrared wavelengths for biomedical and clinical applications.
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Affiliation(s)
- Atam P Dhawan
- Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA.
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47
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Chen Y, Liang CP, Liu Y, Fischer AH, Parwani AV, Pantanowitz L. Review of advanced imaging techniques. J Pathol Inform 2012; 3:22. [PMID: 22754737 PMCID: PMC3385156 DOI: 10.4103/2153-3539.96751] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2012] [Accepted: 04/28/2012] [Indexed: 12/20/2022] Open
Abstract
Pathology informatics encompasses digital imaging and related applications. Several specialized microscopy techniques have emerged which permit the acquisition of digital images (“optical biopsies”) at high resolution. Coupled with fiber-optic and micro-optic components, some of these imaging techniques (e.g., optical coherence tomography) are now integrated with a wide range of imaging devices such as endoscopes, laparoscopes, catheters, and needles that enable imaging inside the body. These advanced imaging modalities have exciting diagnostic potential and introduce new opportunities in pathology. Therefore, it is important that pathology informaticists understand these advanced imaging techniques and the impact they have on pathology. This paper reviews several recently developed microscopic techniques, including diffraction-limited methods (e.g., confocal microscopy, 2-photon microscopy, 4Pi microscopy, and spatially modulated illumination microscopy) and subdiffraction techniques (e.g., photoactivated localization microscopy, stochastic optical reconstruction microscopy, and stimulated emission depletion microscopy). This article serves as a primer for pathology informaticists, highlighting the fundamentals and applications of advanced optical imaging techniques.
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Affiliation(s)
- Yu Chen
- Fischell Department of Bioengineering, University of Maryland, College Park, MD, USA
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48
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Zhang JG, Liu HF. Functional imaging and endoscopy. World J Gastroenterol 2011; 17:4277-82. [PMID: 22090783 PMCID: PMC3214702 DOI: 10.3748/wjg.v17.i38.4277] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2011] [Revised: 05/20/2011] [Accepted: 05/27/2011] [Indexed: 02/06/2023] Open
Abstract
The emergence of endoscopy for the diagnosis of gastrointestinal diseases and the treatment of gastrointestinal diseases has brought great changes. The mere observation of anatomy with the imaging mode using modern endoscopy has played a significant role in this regard. However, increasing numbers of endoscopies have exposed additional deficiencies and defects such as anatomically similar diseases. Endoscopy can be used to examine lesions that are difficult to identify and diagnose. Early disease detection requires that substantive changes in biological function should be observed, but in the absence of marked morphological changes, endoscopic detection and diagnosis are difficult. Disease detection requires not only anatomic but also functional imaging to achieve a comprehensive interpretation and understanding. Therefore, we must ask if endoscopic examination can be integrated with both anatomic imaging and functional imaging. In recent years, as molecular biology and medical imaging technology have further developed, more functional imaging methods have emerged. This paper is a review of the literature related to endoscopic optical imaging methods in the hopes of initiating integration of functional imaging and anatomical imaging to yield a new and more effective type of endoscopy.
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49
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Kang W, Wang H, Wang Z, Jenkins MW, Isenberg GA, Chak A, Rollins AM. Motion artifacts associated with in vivo endoscopic OCT images of the esophagus. OPTICS EXPRESS 2011; 19:20722-35. [PMID: 21997082 PMCID: PMC3495872 DOI: 10.1364/oe.19.020722] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
3-D optical coherence tomography (OCT) has been extensively investigated as a potential screening and/or surveillance tool for Barrett's esophagus (BE). Understanding and correcting motion artifact may improve image interpretation. In this work, the motion trace was analyzed to show the physiological origin (respiration and heart beat) of the artifacts. Results showed that increasing balloon pressure did not sufficiently suppress the physiological motion artifact. An automated registration algorithm was designed to correct such artifacts. The performance of the algorithm was evaluated in images of normal porcine esophagus and demonstrated in images of BE in human patients.
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Affiliation(s)
- Wei Kang
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106,
USA
- These authors contributed equally to this work
| | - Hui Wang
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106,
USA
- These authors contributed equally to this work
| | - Zhao Wang
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106,
USA
| | - Michael W. Jenkins
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106,
USA
| | - Gerard A. Isenberg
- Department of Medicine, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106,
USA
| | - Amitabh Chak
- Department of Medicine, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106,
USA
| | - Andrew M. Rollins
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106,
USA
- Department of Medicine, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106,
USA
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50
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Garcia-Allende PB, Amygdalos I, Dhanapala H, Goldin RD, Hanna GB, Elson DS. Morphological analysis of optical coherence tomography images for automated classification of gastrointestinal tissues. BIOMEDICAL OPTICS EXPRESS 2011; 2:2821-36. [PMID: 22091441 PMCID: PMC3191449 DOI: 10.1364/boe.2.002821] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2011] [Revised: 09/09/2011] [Accepted: 09/15/2011] [Indexed: 05/20/2023]
Abstract
The impact of digestive diseases, which include disorders affecting the oropharynx and alimentary canal, ranges from the inconvenience of a transient diarrhoea to dreaded conditions such as pancreatic cancer, which are usually fatal. Currently, the major limitation for the diagnosis of such diseases is sampling error because, even in the cases of rigorous adherence to biopsy protocols, only a tiny fraction of the surface of the involved gastrointestinal tract is sampled. Optical coherence tomography (OCT), which is an interferometric imaging technique for the minimally invasive measurement of biological samples, could decrease sampling error, increase yield, and even eliminate the need for tissue sampling provided that an automated, quick and reproducible tissue classification system is developed. Segmentation and quantification of ophthalmologic pathologies using OCT traditionally rely on the extraction of thickness and size measures from the OCT images, but layers are often not observed in nonopthalmic OCT imaging. Distinct mathematical methods, namely Principal Component Analysis (PCA) and textural analyses including both spatial textural analysis derived from the two-dimensional discrete Fourier transform (DFT) and statistical texture analysis obtained independently from center-symmetric autocorrelation (CSAC) and spatial grey-level dependency matrices (SGLDM), have been previously reported to overcome this problem. We propose an alternative approach consisting of a region segmentation according to the intensity variation along the vertical axis and a pure statistical technique for feature quantification, i.e. morphological analysis. Qualitative and quantitative comparisons with traditional approaches are accomplished in the discrimination of freshly-excised specimens of gastrointestinal tissues to exhibit the feasibility of the proposed method for computer-aided diagnosis (CAD) in the clinical setting.
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