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1
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Galli R, Uckermann O. Toward cancer detection by label-free microscopic imaging in oncological surgery: Techniques, instrumentation and applications. Micron 2025; 191:103800. [PMID: 39923310 DOI: 10.1016/j.micron.2025.103800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2024] [Revised: 01/31/2025] [Accepted: 02/03/2025] [Indexed: 02/11/2025]
Abstract
This review examines the clinical application of label-free microscopy and spectroscopy, which are based on optical signals emitted by tissue components. Over the past three decades, a variety of techniques have been investigated with the aim of developing an in situ histopathology method that can rapidly and accurately identify tumor margins during surgical procedures. These techniques can be divided into two groups. One group encompasses techniques exploiting linear optical signals, and includes infrared and Raman microspectroscopy, and autofluorescence microscopy. The second group includes techniques based on nonlinear optical signals, including harmonic generation, coherent Raman scattering, and multiphoton autofluorescence microscopy. Some of these methods provide comparable information, while others are complementary. However, all of them have distinct advantages and disadvantages due to their inherent nature. The first part of the review provides an explanation of the underlying physics of the excitation mechanisms and a description of the instrumentation. It also covers endomicroscopy and data analysis, which are important for understanding the current limitations in implementing label-free techniques in clinical settings. The second part of the review describes the application of label-free microscopy imaging to improve oncological surgery with focus on brain tumors and selected gastrointestinal cancers, and provides a critical assessment of the current state of translation of these methods into clinical practice. Finally, the potential of confocal laser endomicroscopy for the acquisition of autofluorescence is discussed in the context of immediate clinical applications.
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Affiliation(s)
- Roberta Galli
- Medical Physics and Biomedical Engineering, Faculty of Medicine, TU Dresden, Fetscherstr. 74, Dresden 01307, Germany.
| | - Ortrud Uckermann
- Department of Neurosurgery, University Hospital Carl Gustav Carus, TU Dresden, Fetscherstr. 74, Dresden 01307, Germany
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2
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Priyadarshini K, Ali SA, Sivanandam K, Alagarsamy M. Human lung cancer classification and comprehensive analysis using different machine learning techniques. Microsc Res Tech 2025; 88:234-250. [PMID: 39295255 DOI: 10.1002/jemt.24682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 06/14/2024] [Accepted: 08/16/2024] [Indexed: 09/21/2024]
Abstract
Lung cancer is the most common causes of death among all cancer-related diseases. A lung scan examination of the patient is the primary diagnostic technique. This scan analysis pertains to an MRI, CT, or X-ray. The automated classification of lung cancer is difficult due to the involvement of multiple steps in imaging patients' lungs. In this manuscript, human lung cancer classification and comprehensive analysis using different machine learning techniques is proposed. Initially, the input images are gathered using lung cancer dataset. The proposed method processes these images using image-processing techniques, and further machine learning techniques are utilized for categorization. Seven different classifiers including the k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), multinomial naive Bayes (MNB), stochastic gradient descent (SGD), random forest (RF), and multi-layer perceptron (MLP) classifier are used, which classifies the lung cancer as malignant and benign. The performance of the proposed approach is examined using performances metrics, like positive predictive value, accuracy, sensitivity, and f-score are evaluated. Among them, the performance of the MLP classifier provides 25.34%, 45.39%, 15.39%, 41.28%, 22.17%, and 12.12% higher accuracy than other KNN, SVM, DT, MNB, SGD, and RF respectively. RESEARCH HIGHLIGHTS: Lung cancer is a leading cause of cancer-related death. Imaging (MRI, CT, and X-ray) aids diagnosis. Automated classification of lung cancer faces challenges due to complex imaging steps. This study proposes human lung cancer classification using diverse machine learning techniques. Input images from lung cancer dataset undergo image processing and machine learning. Classifiers like k-nearest neighbors, support vector machine, decision tree, multinomial naive Bayes, stochastic gradient descent, random forest, and multi-layer perceptron (MLP) classify cancer types; MLP excels in accuracy.
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Affiliation(s)
- K Priyadarshini
- Department of Electronics and Communication Engineering, K. Ramakrishnan College of Engineering, Trichy, Tamilnadu, India
| | - S Ahamed Ali
- Department of Computer Science and Engineering, Easwari Engineering College, Chennai, Tamilnadu, India
| | - K Sivanandam
- Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur, Tamilnadu, India
| | - Manjunathan Alagarsamy
- Department of Electronics and Communication Engineering, K. Ramakrishnan College of Technology, Trichy, Tamilnadu, India
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3
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Yang X, Chen A, U K, Zhang SM, Wang P, Li Z, Luo Y, Cui Y. Optical sensor for fast and accurate lung cancer detection with tissue autofluorescence and diffuse reflectance spectroscopy. Thorac Cancer 2025; 16:e15476. [PMID: 39558507 PMCID: PMC11729394 DOI: 10.1111/1759-7714.15476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2024] [Revised: 09/27/2024] [Accepted: 10/13/2024] [Indexed: 11/20/2024] Open
Abstract
BACKGROUND Cancer is a severe threat to human health, and surgery is a major method of cancer treatment. This study aimed to develop an optical sensor for fast cancer tissue. METHODS The tissue autofluorescence spectrum and diffuse reflectance spectrum were obtained by using a laboratory-developed optical sensor system. A total of 151 lung tissue samples were used in this ex vivo study. RESULTS Experimental results demonstrate that tissue autofluorescence spectroscopy with a 365-nm excitation has better performance than diffuse reflectance spectroscopy, and 63 of 64 test samples (98.4% accuracy) were correctly classified with tissue autofluorescence spectroscopy and our developed data analysis method. CONCLUSIONS Our promising ex vivo study results show that the developed optical sensor system has great promise for future clinical translation for intraoperative lung cancer detection and other applications.
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Affiliation(s)
- Xianbei Yang
- Department of Thoracic SurgeryBeijing Friendship Hospital, Capital Medical UniversityBeijingChina
| | - Anzhi Chen
- School of Computer Science and Technology, North China University of TechnologyBeijingChina
| | - Kaicheng U
- Department of Computational BiologyCollege of Agriculture and Life Sciences, Cornell UniversityIthacaNew YorkUSA
| | - Sophia Meixuan Zhang
- Department of Biological SciencesCollege of Agriculture and Life Sciences, Cornell UniversityIthacaNew YorkUSA
| | - Peihao Wang
- Department of Thoracic SurgeryBeijing Friendship Hospital, Capital Medical UniversityBeijingChina
| | - Zheng Li
- Department of Agricultural and Resource EconomicsCollege of Agriculture and Life Sciences, North Carolina State UniversityRaleighNorth CarolinaUSA
| | - Yi Luo
- Department of Thoracic SurgeryUniversity‐Town Hospital of Chongqing Medical UniversityChongqingChina
| | - Yong Cui
- Department of Thoracic SurgeryBeijing Friendship Hospital, Capital Medical UniversityBeijingChina
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4
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Dzelve P, Legzdiņa A, Krūmiņa A, Tirzīte M. Utility of Raman Spectroscopy in Pulmonary Medicine. Adv Respir Med 2024; 92:421-428. [PMID: 39452060 PMCID: PMC11505626 DOI: 10.3390/arm92050038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2024] [Revised: 10/04/2024] [Accepted: 10/14/2024] [Indexed: 10/26/2024]
Abstract
The Raman effect, or as per its original description, "modified scattering", is an observation that the number of scattered light waves shifts after photons make nonelastic contact with a molecule. This effect allows Raman spectroscopy to be very useful in various fields. Although it is well known that Raman spectroscopy could be very beneficial in medicine as a diagnostic tool, there are not many applications of Raman spectroscopy in pulmonary medicine. Mostly tumor tissue, sputum and saliva have been used as material for analysis in respiratory medicine. Raman spectroscopy has shown promising results in malignancy recognition and even tumor staging. Saliva is a biological fluid that could be used as a reliable biomarker of the physiological state of the human body, and is easily acquired. Saliva analysis using Raman spectroscopy has the potential to be a relatively inexpensive and quick tool that could be used for diagnostic, screening and phenotyping purposes. Chronic obstructive pulmonary disease (COPD) is a growing cause of disability and death, and its phenotyping using saliva analysis via Raman spectroscopy has a great potential to be a dependable tool to, among other things, help reduce hospitalizations and disease burden. Although existing methods are effective and generally available, Raman spectroscopy has the benefit of being quick and noninvasive, potentially reducing healthcare costs and workload.
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Affiliation(s)
- Pauls Dzelve
- Department of Internal Medicine, Faculty of Medicine, Riga Stradiņš University, LV1007 Riga, Latvia; (A.L.); (A.K.); (M.T.)
- Clinical Centre “Gaiļezers”, Riga East University Hospital, LV1038 Riga, Latvia
| | - Arta Legzdiņa
- Department of Internal Medicine, Faculty of Medicine, Riga Stradiņš University, LV1007 Riga, Latvia; (A.L.); (A.K.); (M.T.)
- Clinical Centre “Gaiļezers”, Riga East University Hospital, LV1038 Riga, Latvia
| | - Andra Krūmiņa
- Department of Internal Medicine, Faculty of Medicine, Riga Stradiņš University, LV1007 Riga, Latvia; (A.L.); (A.K.); (M.T.)
- Clinical Centre “Gaiļezers”, Riga East University Hospital, LV1038 Riga, Latvia
| | - Madara Tirzīte
- Department of Internal Medicine, Faculty of Medicine, Riga Stradiņš University, LV1007 Riga, Latvia; (A.L.); (A.K.); (M.T.)
- Clinical Centre “Gaiļezers”, Riga East University Hospital, LV1038 Riga, Latvia
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5
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Moradi F, van den Berg M, Mirjebreili M, Kosten L, Verhoye M, Amiri M, Keliris GA. Early classification of Alzheimer's disease phenotype based on hippocampal electrophysiology in the TgF344-AD rat model. iScience 2023; 26:107454. [PMID: 37599835 PMCID: PMC10432721 DOI: 10.1016/j.isci.2023.107454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 04/27/2023] [Accepted: 07/19/2023] [Indexed: 08/22/2023] Open
Abstract
The hippocampus plays a vital role in navigation, learning, and memory, and is affected in Alzheimer's disease (AD). This study investigated the classification of AD-transgenic rats versus wild-type littermates using electrophysiological activity recorded from the hippocampus at an early, presymptomatic stage of the disease (6 months old) in the TgF344-AD rat model. The recorded signals were filtered into low frequency (LFP) and high frequency (spiking activity) signals, and machine learning classifiers were employed to identify the rat genotype (TG vs. WT). By analyzing specific frequency bands in the low frequency signals and calculating distance metrics between spike trains in the high frequency signals, accurate classification was achieved. Gamma band power emerged as a valuable signal for classification, and combining information from both low and high frequency signals improved the accuracy further. These findings provide valuable insights into the early stage effects of AD on different regions of the hippocampus.
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Affiliation(s)
- Faraz Moradi
- Faculty of Engineering, University of Ottawa, Ottawa, ON, Canada
| | - Monica van den Berg
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | | | - Lauren Kosten
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Marleen Verhoye
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Mahmood Amiri
- Medical Technology Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Georgios A. Keliris
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
- Institute of Computer Science, Foundation for Research & Technology - Hellas, Heraklion, Crete, Greece
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6
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Lin E, Scherman M, Santagata R, Bresson A, Attal-Tretout B. Birefringence based multi-focus fs/ps-CARS spectroscopy for thermometry and hyperspectral microscopy. OPTICS EXPRESS 2023; 31:11899-11912. [PMID: 37155814 DOI: 10.1364/oe.485446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
We present a multi-focus fs/ps-CARS scheme to perform spectroscopy on multiple points simultaneously for gas phase measurements and microscopy, using a single birefringence crystal or a combination of birefringent stacks. CARS performances are first reported for 1 kHz single-shot N2 spectroscopy on two points set few millimeters apart, allowing thermometry measurements to be carried out in the vicinity of a flame. Then, simultaneous acquisition of toluene spectra is demonstrated on two points set 14 µm apart in a microscope setup. Finally, two-point and four-point hyperspectral imaging of PMMA microbeads in water is performed, demonstrating a proportional increase in acquisition speed.
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7
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Atasever S, Azginoglu N, Terzi DS, Terzi R. A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning. Clin Imaging 2023; 94:18-41. [PMID: 36462229 DOI: 10.1016/j.clinimag.2022.11.003] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 10/17/2022] [Accepted: 11/01/2022] [Indexed: 11/13/2022]
Abstract
This survey aims to identify commonly used methods, datasets, future trends, knowledge gaps, constraints, and limitations in the field to provide an overview of current solutions used in medical image analysis in parallel with the rapid developments in transfer learning (TL). Unlike previous studies, this survey grouped the last five years of current studies for the period between January 2017 and February 2021 according to different anatomical regions and detailed the modality, medical task, TL method, source data, target data, and public or private datasets used in medical imaging. Also, it provides readers with detailed information on technical challenges, opportunities, and future research trends. In this way, an overview of recent developments is provided to help researchers to select the most effective and efficient methods and access widely used and publicly available medical datasets, research gaps, and limitations of the available literature.
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Affiliation(s)
- Sema Atasever
- Computer Engineering Department, Nevsehir Hacı Bektas Veli University, Nevsehir, Turkey.
| | - Nuh Azginoglu
- Computer Engineering Department, Kayseri University, Kayseri, Turkey.
| | | | - Ramazan Terzi
- Computer Engineering Department, Amasya University, Amasya, Turkey.
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8
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Advanced Surgical Technologies for Lung Cancer Treatment: Current Status and Perspectives. ENGINEERED REGENERATION 2022. [DOI: 10.1016/j.engreg.2022.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
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9
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Kaothanthong N, Atsavasirilert K, Sarampakhul S, Chantangphol P, Songsaeng D, Makhanov S. Artificial intelligence for localization of the acute ischemic stroke by non-contrast computed tomography. PLoS One 2022; 17:e0277573. [PMID: 36454916 PMCID: PMC9714826 DOI: 10.1371/journal.pone.0277573] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 10/29/2022] [Indexed: 12/03/2022] Open
Abstract
A non-contrast cranial computer tomography (ncCT) is often employed for the diagnosis of the early stage of the ischemic stroke. However, the number of false negatives is high. More accurate results are obtained by an MRI. However, the MRI is not available in every hospital. Moreover, even if it is available in the clinic for the routine tests, emergency often does not have it. Therefore, this paper proposes an end-to-end framework for detection and segmentation of the brain infarct on the ncCT. The computer tomography perfusion (CTp) is used as the ground truth. The proposed ensemble model employs three deep convolution neural networks (CNNs) to process three end-to-end feature maps and a hand-craft features characterized by specific contra-lateral features. To improve the accuracy of the detected infarct area, the spatial dependencies between neighboring slices are employed at the postprocessing step. The numerical experiments have been performed on 18 ncCT-CTp paired stroke cases (804 image-pairs). The leave-one-out approach is applied for evaluating the proposed method. The model achieves 91.16% accuracy, 65.15% precision, 77.44% recall, 69.97% F1 score, and 0.4536 IoU.
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Affiliation(s)
- Natsuda Kaothanthong
- Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand
| | - Kamin Atsavasirilert
- Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand
| | - Soawapot Sarampakhul
- Division of Diagnostic Radiology, Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Pantid Chantangphol
- Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand
| | - Dittapong Songsaeng
- Division of Diagnostic Radiology, Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Stanislav Makhanov
- Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand
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10
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Golovanevsky M, Eickhoff C, Singh R. Multimodal attention-based deep learning for Alzheimer's disease diagnosis. J Am Med Inform Assoc 2022; 29:2014-2022. [PMID: 36149257 PMCID: PMC9667156 DOI: 10.1093/jamia/ocac168] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/10/2022] [Accepted: 09/21/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Alzheimer's disease (AD) is the most common neurodegenerative disorder with one of the most complex pathogeneses, making effective and clinically actionable decision support difficult. The objective of this study was to develop a novel multimodal deep learning framework to aid medical professionals in AD diagnosis. MATERIALS AND METHODS We present a Multimodal Alzheimer's Disease Diagnosis framework (MADDi) to accurately detect the presence of AD and mild cognitive impairment (MCI) from imaging, genetic, and clinical data. MADDi is novel in that we use cross-modal attention, which captures interactions between modalities-a method not previously explored in this domain. We perform multi-class classification, a challenging task considering the strong similarities between MCI and AD. We compare with previous state-of-the-art models, evaluate the importance of attention, and examine the contribution of each modality to the model's performance. RESULTS MADDi classifies MCI, AD, and controls with 96.88% accuracy on a held-out test set. When examining the contribution of different attention schemes, we found that the combination of cross-modal attention with self-attention performed the best, and no attention layers in the model performed the worst, with a 7.9% difference in F1-scores. DISCUSSION Our experiments underlined the importance of structured clinical data to help machine learning models contextualize and interpret the remaining modalities. Extensive ablation studies showed that any multimodal mixture of input features without access to structured clinical information suffered marked performance losses. CONCLUSION This study demonstrates the merit of combining multiple input modalities via cross-modal attention to deliver highly accurate AD diagnostic decision support.
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Affiliation(s)
- Michal Golovanevsky
- Department of Computer Science, Brown University, Providence, Rhode Island, USA
| | - Carsten Eickhoff
- Department of Computer Science, Brown University, Providence, Rhode Island, USA
- Center for Biomedical Informatics, Brown University, Providence, Rhode Island, USA
| | - Ritambhara Singh
- Department of Computer Science, Brown University, Providence, Rhode Island, USA
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, USA
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11
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Arano-Martinez JA, Martínez-González CL, Salazar MI, Torres-Torres C. A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning. BIOSENSORS 2022; 12:710. [PMID: 36140093 PMCID: PMC9496380 DOI: 10.3390/bios12090710] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 08/22/2022] [Accepted: 08/25/2022] [Indexed: 11/25/2022]
Abstract
The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells.
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Affiliation(s)
- Jose Alberto Arano-Martinez
- Sección de Estudios de Posgrado e Investigación, Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Zacatenco, Instituto Politécnico Nacional, Mexico City 07738, Mexico
| | - Claudia Lizbeth Martínez-González
- Sección de Estudios de Posgrado e Investigación, Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Zacatenco, Instituto Politécnico Nacional, Mexico City 07738, Mexico
| | - Ma Isabel Salazar
- Departamento de Microbiología, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Mexico City 11340, Mexico
| | - Carlos Torres-Torres
- Sección de Estudios de Posgrado e Investigación, Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Zacatenco, Instituto Politécnico Nacional, Mexico City 07738, Mexico
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12
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Bucharskaya AB, Yanina IY, Atsigeida SV, Genin VD, Lazareva EN, Navolokin NA, Dyachenko PA, Tuchina DK, Tuchina ES, Genina EA, Kistenev YV, Tuchin VV. Optical clearing and testing of lung tissue using inhalation aerosols: prospects for monitoring the action of viral infections. Biophys Rev 2022; 14:1005-1022. [PMID: 36042751 PMCID: PMC9415257 DOI: 10.1007/s12551-022-00991-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 08/03/2022] [Indexed: 02/06/2023] Open
Abstract
Optical clearing of the lung tissue aims to make it more transparent to light by minimizing light scattering, thus allowing reconstruction of the three-dimensional structure of the tissue with a much better resolution. This is of great importance for monitoring of viral infection impact on the alveolar structure of the tissue and oxygen transport. Optical clearing agents (OCAs) can provide not only lesser light scattering of tissue components but also may influence the molecular transport function of the alveolar membrane. Air-filled lungs present significant challenges for optical imaging including optical coherence tomography (OCT), confocal and two-photon microscopy, and Raman spectroscopy, because of the large refractive-index mismatch between alveoli walls and the enclosed air-filled region. During OCT imaging, the light is strongly backscattered at each air–tissue interface, such that image reconstruction is typically limited to a single alveolus. At the same time, the filling of these cavities with an OCA, to which water (physiological solution) can also be attributed since its refractive index is much higher than that of air will lead to much better tissue optical transmittance. This review presents general principles and advances in the field of tissue optical clearing (TOC) technology, OCA delivery mechanisms in lung tissue, studies of the impact of microbial and viral infections on tissue response, and antimicrobial and antiviral photodynamic therapies using methylene blue (MB) and indocyanine green (ICG) dyes as photosensitizers.
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Affiliation(s)
- Alla B. Bucharskaya
- Centre of Collective Use, Saratov State Medical University n.a. V.I. Razumovsky, 112 B. Kazach’ya, Saratov, 410012 Russia
- Science Medical Center, Saratov State University, 83 Astrakhanskaya St, Saratov, 410012 Russia
- Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 36 Lenin’s Av, Tomsk, 634050 Russia
| | - Irina Yu. Yanina
- Science Medical Center, Saratov State University, 83 Astrakhanskaya St, Saratov, 410012 Russia
- Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 36 Lenin’s Av, Tomsk, 634050 Russia
| | - Sofia V. Atsigeida
- Science Medical Center, Saratov State University, 83 Astrakhanskaya St, Saratov, 410012 Russia
- Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 36 Lenin’s Av, Tomsk, 634050 Russia
| | - Vadim D. Genin
- Science Medical Center, Saratov State University, 83 Astrakhanskaya St, Saratov, 410012 Russia
- Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 36 Lenin’s Av, Tomsk, 634050 Russia
| | - Ekaterina N. Lazareva
- Science Medical Center, Saratov State University, 83 Astrakhanskaya St, Saratov, 410012 Russia
- Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 36 Lenin’s Av, Tomsk, 634050 Russia
| | - Nikita A. Navolokin
- Centre of Collective Use, Saratov State Medical University n.a. V.I. Razumovsky, 112 B. Kazach’ya, Saratov, 410012 Russia
- Science Medical Center, Saratov State University, 83 Astrakhanskaya St, Saratov, 410012 Russia
| | - Polina A. Dyachenko
- Science Medical Center, Saratov State University, 83 Astrakhanskaya St, Saratov, 410012 Russia
- Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 36 Lenin’s Av, Tomsk, 634050 Russia
| | - Daria K. Tuchina
- Science Medical Center, Saratov State University, 83 Astrakhanskaya St, Saratov, 410012 Russia
- Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 36 Lenin’s Av, Tomsk, 634050 Russia
| | - Elena S. Tuchina
- Department of Biology, Saratov State University, 83 Astrakhanskaya St, Saratov, 410012 Russia
| | - Elina A. Genina
- Science Medical Center, Saratov State University, 83 Astrakhanskaya St, Saratov, 410012 Russia
- Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 36 Lenin’s Av, Tomsk, 634050 Russia
| | - Yury V. Kistenev
- Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 36 Lenin’s Av, Tomsk, 634050 Russia
| | - Valery V. Tuchin
- Science Medical Center, Saratov State University, 83 Astrakhanskaya St, Saratov, 410012 Russia
- Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 36 Lenin’s Av, Tomsk, 634050 Russia
- Laboratory of Laser Diagnostics of Technical and Living Systems, Institute of Precision Mechanics and Control, FRC “Saratov Scientific Centre of the Russian Academy of Sciences”, 24 Rabochaya St, Saratov, 410028 Russia
- A.N. Bach Institute of Biochemistry, FRC “Fundamentals of Biotechnology” of the Russian Academy of Sciences, 33-2 Leninsky Av, Moscow, 119991 Russia
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13
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Krishna R, Colak I. Advances in Biomedical Applications of Raman Microscopy and Data Processing: A Mini Review. ANAL LETT 2022. [DOI: 10.1080/00032719.2022.2094391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Ram Krishna
- Department of Mechanical Engineering, Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh, India
- Electrical and Electronics Engineering, Nisantasi University, Istanbul, Turkey
- Ohm Janki Biotech Research Private Limited, India
| | - Ilhami Colak
- Electrical and Electronics Engineering, Nisantasi University, Istanbul, Turkey
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14
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Melanthota SK, Gopal D, Chakrabarti S, Kashyap AA, Radhakrishnan R, Mazumder N. Deep learning-based image processing in optical microscopy. Biophys Rev 2022; 14:463-481. [PMID: 35528030 PMCID: PMC9043085 DOI: 10.1007/s12551-022-00949-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 03/14/2022] [Indexed: 12/19/2022] Open
Abstract
Optical microscopy has emerged as a key driver of fundamental research since it provides the ability to probe into imperceptible structures in the biomedical world. For the detailed investigation of samples, a high-resolution image with enhanced contrast and minimal damage is preferred. To achieve this, an automated image analysis method is preferable over manual analysis in terms of both speed of acquisition and reduced error accumulation. In this regard, deep learning (DL)-based image processing can be highly beneficial. The review summarises and critiques the use of DL in image processing for the data collected using various optical microscopic techniques. In tandem with optical microscopy, DL has already found applications in various problems related to image classification and segmentation. It has also performed well in enhancing image resolution in smartphone-based microscopy, which in turn enablse crucial medical assistance in remote places. Graphical abstract
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Affiliation(s)
- Sindhoora Kaniyala Melanthota
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka 576104 India
| | - Dharshini Gopal
- Department of Bioinformatics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka 576104 India
| | - Shweta Chakrabarti
- Department of Bioinformatics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka 576104 India
| | - Anirudh Ameya Kashyap
- Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104 India
| | - Raghu Radhakrishnan
- Department of Oral Pathology, Manipal College of Dental Sciences, Manipal, Manipal Academy of Higher Education, Manipal, 576104 India
| | - Nirmal Mazumder
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka 576104 India
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15
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Abdolghader P, Ridsdale A, Grammatikopoulos T, Resch G, Légaré F, Stolow A, Pegoraro AF, Tamblyn I. Unsupervised hyperspectral stimulated Raman microscopy image enhancement: denoising and segmentation via one-shot deep learning. OPTICS EXPRESS 2021; 29:34205-34219. [PMID: 34809216 DOI: 10.1364/oe.439662] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 09/26/2021] [Indexed: 06/13/2023]
Abstract
Hyperspectral stimulated Raman scattering (SRS) microscopy is a label-free technique for biomedical and mineralogical imaging which can suffer from low signal-to-noise ratios. Here we demonstrate the use of an unsupervised deep learning neural network for rapid and automatic denoising of SRS images: UHRED (Unsupervised Hyperspectral Resolution Enhancement and Denoising). UHRED is capable of "one-shot" learning; only one hyperspectral image is needed, with no requirements for training on previously labelled datasets or images. Furthermore, by applying a k-means clustering algorithm to the processed data, we demonstrate automatic, unsupervised image segmentation, yielding, without prior knowledge of the sample, intuitive chemical species maps, as shown here for a lithium ore sample.
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16
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Li Z, Li Z, Chen Q, Ramos A, Zhang J, Boudreaux JP, Thiagarajan R, Bren-Mattison Y, Dunham ME, McWhorter AJ, Li X, Feng JM, Li Y, Yao S, Xu J. Detection of pancreatic cancer by convolutional-neural-network-assisted spontaneous Raman spectroscopy with critical feature visualization. Neural Netw 2021; 144:455-464. [PMID: 34583101 DOI: 10.1016/j.neunet.2021.09.006] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 08/23/2021] [Accepted: 09/03/2021] [Indexed: 02/02/2023]
Abstract
Pancreatic cancer is the deadliest cancer type with a five-year survival rate of less than 9%. Detection of tumor margins plays an essential role in the success of surgical resection. However, histopathological assessment is time-consuming, expensive, and labor-intensive. We constructed a lab-designed, hand-held Raman spectroscopic system that could enable intraoperative tissue diagnosis using convolutional neural network (CNN) models to efficiently distinguish between cancerous and normal pancreatic tissue. To our best knowledge, this is the first reported effort to diagnose pancreatic cancer by CNN-aided spontaneous Raman scattering with a lab-developed system designed for intraoperative applications. Classification based on the original one-dimensional (1D) Raman, two-dimensional (2D) Raman images, and the first principal component (PC1) from the principal component analysis on the 2D image, could all achieve high performance: the testing sensitivity, specificity, and accuracy were over 95%, and the area under the curve approached 0.99. Although CNN models often show great success in classification, it has always been challenging to visualize the CNN features in these models, which has never been achieved in the Raman spectroscopy application in cancer diagnosis. By studying individual Raman regions and by extracting and visualizing CNN features from max-pooling layers, we identified critical Raman peaks that could aid in the classification of cancerous and noncancerous tissues. 2D Raman PC1 yielded more critical peaks for pancreatic cancer identification than that of 1D Raman, as the Raman intensity was amplified by 2D Raman PC1. To our best knowledge, the feature visualization was achieved for the first time in the field of CNN-aided spontaneous Raman spectroscopy for cancer diagnosis. Based on these CNN feature peaks and their frequency at specific wavenumbers, pancreatic cancerous tissue was found to contain more biochemical components related to the protein contents (particularly collagen), whereas normal pancreatic tissue was found to contain more lipids and nucleic acid (particularly deoxyribonucleic acid/ribonucleic acid). Overall, the CNN model in combination with Raman spectroscopy could serve as a useful tool for the extraction of key features that can help differentiate pancreatic cancer from a normal pancreas.
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Affiliation(s)
- Zhongqiang Li
- Division of Electrical and Computer Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Zheng Li
- Division of Electrical and Computer Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Qing Chen
- Division of Computer Science & Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Alexandra Ramos
- Department of Comparative Biomedical Science, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Jian Zhang
- Division of Computer Science & Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
| | - J Philip Boudreaux
- Department of Surgery, School of Medicine, Louisiana State University Health Science Center, New Orleans, LA 70112, USA
| | - Ramcharan Thiagarajan
- Department of Surgery, School of Medicine, Louisiana State University Health Science Center, New Orleans, LA 70112, USA
| | - Yvette Bren-Mattison
- Department of Surgery, School of Medicine, Louisiana State University Health Science Center, New Orleans, LA 70112, USA
| | - Michael E Dunham
- Department of Otolaryngology, School of Medicine, Louisiana State University Health Science Center, New Orleans, LA 70112, USA
| | - Andrew J McWhorter
- Department of Otolaryngology, School of Medicine, Louisiana State University Health Science Center, New Orleans, LA 70112, USA
| | - Xin Li
- Division of Electrical and Computer Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Ji-Ming Feng
- Department of Comparative Biomedical Science, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Yanping Li
- School of Environment and Sustainability, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada
| | - Shaomian Yao
- Department of Comparative Biomedical Science, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Jian Xu
- Division of Electrical and Computer Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA 70803, USA.
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17
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Li C. Construction of the Reverse Resource Recovery System of e-Waste Based on DLRNN. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:2143235. [PMID: 34603427 PMCID: PMC8486507 DOI: 10.1155/2021/2143235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 09/09/2021] [Accepted: 09/11/2021] [Indexed: 11/17/2022]
Abstract
The research on the reverse resource network of e-waste at home and abroad is still in its infancy, and most of it is only based on traditional forward logistics. Reverse resources are the process of moving goods from their typical final destination for recycling value or proper disposal. With the intensification of market competition and the strengthening of environmental protection legislation by the government, reverse resources are no longer a neglected corner in the supply chain. The DLRNN model of the e-waste reverse resource recovery system constructed in this paper can provide an important theoretical and empirical basis for the rational utilization of waste electronic products and fully tap the potential value of waste electronic products, which is of great significance to the recycling of natural resources. In this paper, a hybrid network framework DLRNN based on deep learning (DL) and cyclic neural network (RNN) is designed for problem classification. Experimental results show that the classification accuracy of this framework is improved by 2.4% on TREC and 2.5% on MSQC without additional word vector conversion tools.
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Affiliation(s)
- Changru Li
- School of Public Administration, Hohai University, Focheng West Road, Nanjing 211100, China
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18
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Yu S, Li X, Lu W, Li H, Fu YV, Liu F. Analysis of Raman Spectra by Using Deep Learning Methods in the Identification of Marine Pathogens. Anal Chem 2021; 93:11089-11098. [PMID: 34339167 DOI: 10.1021/acs.analchem.1c00431] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
The need for efficient and accurate identification of pathogens in seafood and the environment has become increasingly urgent, given the current global pandemic. Traditional methods are not only time consuming but also lead to sample wastage. Here, we have proposed two new methods that involve Raman spectroscopy combined with a long short-term memory (LSTM) neural network and compared them with a method using a normal convolutional neural network (CNN). We used eight strains isolated from the marine organism Urechis unicinctus, including four kinds of pathogens. After the models were configured and trained, the LSTM methods that we proposed achieved average isolation-level accuracies exceeding 94%, not only meeting the requirement for identification but also indicating that the proposed methods were faster and more accurate than the normal CNN models. Finally, through a computational approach, we designed a loss function to explore the mechanism reflected by the Raman data, finding the Raman segments that most likely exhibited the characteristics of nucleic acids. These novel experimental results provide insights for developing additional deep learning methods to accurately analyze complex Raman data.
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Affiliation(s)
- Shixiang Yu
- Key Laboratory of Coastal Biology and Biological Resources Utilization, CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, P. R. China.,University of the Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Xin Li
- Key Laboratory of Coastal Biology and Biological Resources Utilization, CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, P. R. China.,University of the Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Weilai Lu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, P. R. China.,University of the Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Hanfei Li
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, P. R. China.,University of the Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Yu Vincent Fu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, P. R. China.,University of the Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Fanghua Liu
- Key Laboratory of Coastal Biology and Biological Resources Utilization, CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, P. R. China.,National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management, Institute of Eco-environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou 510650, P. R. China
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19
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Ogawa H, Hashimoto M. Avoidance of four-wave mixing in optical fiber bundle for coherent anti-Stokes Raman scattering endomicroscopy. OPTICS LETTERS 2021; 46:3356-3359. [PMID: 34264212 DOI: 10.1364/ol.425644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 06/14/2021] [Indexed: 06/13/2023]
Abstract
We propose and demonstrate a method of suppressing four-wave mixing (FWM) in an optical fiber bundle to realize coherent anti-Stokes Raman scattering (CARS) endomicroscopy, which is the leading candidate for a definitive diagnosis of gastrointestinal cancer. Two excitation laser beams with different wavelengths are delivered via different cores to suppress FWM and are then combined with a polarization prism and a dual-wavelength wave plate and are focused to a spot. The background emission from the optical fiber bundle was suppressed to 1/3289, and we demonstrated CARS imaging of a polystyrene bead using the proposed method.
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20
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Doherty T, McKeever S, Al-Attar N, Murphy T, Aura C, Rahman A, O'Neill A, Finn SP, Kay E, Gallagher WM, Watson RWG, Gowen A, Jackman P. Feature fusion of Raman chemical imaging and digital histopathology using machine learning for prostate cancer detection. Analyst 2021; 146:4195-4211. [PMID: 34060548 DOI: 10.1039/d1an00075f] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient's quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP-RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and mean and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization.
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Affiliation(s)
- Trevor Doherty
- Technological University Dublin, School of Computer Science, City Campus, Grangegorman Lower, Dublin 7, Ireland.
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21
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Lizio MG, Boitor R, Notingher I. Selective-sampling Raman imaging techniques for ex vivo assessment of surgical margins in cancer surgery. Analyst 2021; 146:3799-3809. [PMID: 34042924 DOI: 10.1039/d1an00296a] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
One of the main challenges in cancer surgery is to ensure the complete excision of the tumour while sparing as much healthy tissue as possible. Histopathology, the gold-standard technique used to assess the surgical margins on the excised tissue, is often impractical for intra-operative use because of the time-consuming tissue cryo-sectioning and staining, and availability of histopathologists to assess stained tissue sections. Raman micro-spectroscopy is a powerful technique that can detect microscopic residual tumours on ex vivo tissue samples with accuracy, based entirely on intrinsic chemical differences. However, raster-scanning Raman micro-spectroscopy is a slow imaging technique that typically requires long data acquisition times wich are impractical for intra-operative use. Selective-sampling Raman imaging overcomes these limitations by using information regarding the spatial properties of the tissue to reduce the number of Raman spectra. This paper reviews the latest advances in selective-sampling Raman techniques and applications, mainly based on multimodal optical imaging. We also highlight the latest results of clinical integration of a prototype device for non-melanoma skin cancer. These promising results indicate the potential impact of Raman spectroscopy for providing fast and objective assessment of surgical margins, helping surgeons ensure the complete removal of tumour cells while sparing as much healthy tissue as possible.
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Affiliation(s)
- Maria Giovanna Lizio
- School of Physics and Astonomy, University of Nottingham, Nottingham, Nottinghamshire, UK.
| | - Radu Boitor
- School of Physics and Astonomy, University of Nottingham, Nottingham, Nottinghamshire, UK.
| | - Ioan Notingher
- School of Physics and Astonomy, University of Nottingham, Nottingham, Nottinghamshire, UK.
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22
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Shabestri B, Anastasio MA, Fei B, Leblond F. Special Series Guest Editorial: Artificial Intelligence and Machine Learning in Biomedical Optics. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-21-0414. [PMID: 33973425 PMCID: PMC8109026 DOI: 10.1117/1.jbo.26.5.052901] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Accepted: 01/01/2021] [Indexed: 06/12/2023]
Abstract
Guest editors Behrouz Shabestri, Mark Anastasio, Baowei Fei, and Frédéric Leblond provide an overview of the JBO Special Series on Artificial Intelligence Machine Learning in Biomedical Optics.
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Affiliation(s)
- Behrouz Shabestri
- National Institute of Biomedical Imaging and Bioengineering, Maryland, United States
| | | | - Baowei Fei
- University of Texas at Dallas, Texas, United States
- UT Southwestern Medical Center, Texas United States
| | - Frédéric Leblond
- Department of Engineering Physics, Polytechnique Montréal, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
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23
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Li Y, Zhou D, Liu TT, Shen XZ. Application of deep learning in image recognition and diagnosis of gastric cancer. Artif Intell Gastrointest Endosc 2021; 2:12-24. [DOI: 10.37126/aige.v2.i2.12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 03/30/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
In recent years, artificial intelligence has been extensively applied in the diagnosis of gastric cancer based on medical imaging. In particular, using deep learning as one of the mainstream approaches in image processing has made remarkable progress. In this paper, we also provide a comprehensive literature survey using four electronic databases, PubMed, EMBASE, Web of Science, and Cochrane. The literature search is performed until November 2020. This article provides a summary of the existing algorithm of image recognition, reviews the available datasets used in gastric cancer diagnosis and the current trends in applications of deep learning theory in image recognition of gastric cancer. covers the theory of deep learning on endoscopic image recognition. We further evaluate the advantages and disadvantages of the current algorithms and summarize the characteristics of the existing image datasets, then combined with the latest progress in deep learning theory, and propose suggestions on the applications of optimization algorithms. Based on the existing research and application, the label, quantity, size, resolutions, and other aspects of the image dataset are also discussed. The future developments of this field are analyzed from two perspectives including algorithm optimization and data support, aiming to improve the diagnosis accuracy and reduce the risk of misdiagnosis.
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Affiliation(s)
- Yu Li
- Department of Gastroenterology and Hepatology, Zhongshan Hospital Affiliated to Fudan University, Shanghai 200032, China
| | - Da Zhou
- Department of Gastroenterology and Hepatology, Zhongshan Hospital Affiliated to Fudan University, Shanghai 200032, China
| | - Tao-Tao Liu
- Department of Gastroenterology and Hepatology, Zhongshan Hospital Affiliated to Fudan University, Shanghai 200032, China
| | - Xi-Zhong Shen
- Department of Gastroenterology and Hepatology, Zhongshan Hospital Affiliated to Fudan University, Shanghai 200032, China
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24
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Ke ZY, Ning YJ, Jiang ZF, Zhu YY, Guo J, Fan XY, Zhang YB. The efficacy of Raman spectroscopy in lung cancer diagnosis: the first diagnostic meta-analysis. Lasers Med Sci 2021; 37:425-434. [PMID: 33856584 DOI: 10.1007/s10103-021-03275-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 02/10/2021] [Indexed: 01/05/2023]
Abstract
In recent years, many researches have explored the diagnostic value of Raman spectroscopy in multiple types of tumors. However, as an emerging clinical examination method, the diagnostic performance of Raman spectroscopy in lung cancer remains unclear. Relevant diagnostic studies published before 1 June 2020 were retrieved from the Cochrane Library, PubMed, EMBASE, China National Knowledge Internet (CNKI), and WanFang databases. After the literature was screened, two authors extracted the data from eligible studies according to the inclusion and exclusion criteria. Obtained data were pooled and analyzed using Stata 16.0, Meta-DiSc 1.4, and RevMan 5.3 software. Fourteen diagnostic studies were eligible for the pooled analysis which includes 779 patients. Total pooled sensitivity and specificity of Raman spectroscopy in diagnosing lung cancer were 0.92 (95% CI 0.87-0.95) and 0.94 (95% CI 0.88-0.97), respectively. The positive likelihood ratio was 15.2 (95% CI 7.5-30.9), the negative likelihood ratio was 0.09 (95% CI 0.05-0.14), and the area under the curve was 0.97 (95 % CI 0.95-0.98). Subgroup analysis suggested that the sensitivity and specificity of RS when analyzing human tissue, serum, and saliva samples were 0.95 (95% CI 0.88-0.98), 0.97 (95% CI 0.89-0.99), 0.88 (95% CI 0.80-0.93), 0.87 (95% CI 0.78-0.92), 0.91 (95% CI 0.80-0.96), and 0.95 (95% CI 0.73-0.99), respectively. No publication bias or threshold effects were detected in this meta-analysis. This initial meta-analysis indicated that Raman spectroscopy is a highly specific and sensitive diagnostic technology for detecting lung cancer. Further investigations are also needed to focus on real-time detection using Raman spectroscopy under bronchoscopy in vivo. Moreover, large-scale diagnostic studies should be conducted to confirm this conclusion.
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Affiliation(s)
- Zhang-Yan Ke
- Department of Geriatric Respiratory and Critical Care, Institute of Respiratory Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, People's Republic of China
| | - Ya-Jing Ning
- Department of Geriatric Respiratory and Critical Care, Institute of Respiratory Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, People's Republic of China
| | - Zi-Feng Jiang
- Department of Geriatric Respiratory and Critical Care, Institute of Respiratory Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, People's Republic of China
| | - Ying-Ying Zhu
- Department of Geriatric Respiratory and Critical Care, Institute of Respiratory Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, People's Republic of China
| | - Jia Guo
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
| | - Xiao-Yun Fan
- Department of Geriatric Respiratory and Critical Care, Institute of Respiratory Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, People's Republic of China.
| | - Yan-Bei Zhang
- Department of Geriatric Respiratory and Critical Care, Institute of Respiratory Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, People's Republic of China.
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25
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Manco L, Maffei N, Strolin S, Vichi S, Bottazzi L, Strigari L. Basic of machine learning and deep learning in imaging for medical physicists. Phys Med 2021; 83:194-205. [DOI: 10.1016/j.ejmp.2021.03.026] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 03/07/2021] [Accepted: 03/16/2021] [Indexed: 02/08/2023] Open
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26
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He H, Yan S, Lyu D, Xu M, Ye R, Zheng P, Lu X, Wang L, Ren B. Deep Learning for Biospectroscopy and Biospectral Imaging: State-of-the-Art and Perspectives. Anal Chem 2021; 93:3653-3665. [PMID: 33599125 DOI: 10.1021/acs.analchem.0c04671] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
With the advances in instrumentation and sampling techniques, there is an explosive growth of data from molecular and cellular samples. The call to extract more information from the large data sets has greatly challenged the conventional chemometrics method. Deep learning, which utilizes very large data sets for finding hidden features therein and for making accurate predictions for a wide range of applications, has been applied in an unbelievable pace in biospectroscopy and biospectral imaging in the recent 3 years. In this Feature, we first introduce the background and basic knowledge of deep learning. We then focus on the emerging applications of deep learning in the data preprocessing, feature detection, and modeling of the biological samples for spectral analysis and spectroscopic imaging. Finally, we highlight the challenges and limitations in deep learning and the outlook for future directions.
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Affiliation(s)
- Hao He
- School of Aerospace Engineering, Xiamen University, Xiamen, 361000, China
| | - Sen Yan
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Danya Lyu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Mengxi Xu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Ruiqian Ye
- School of Aerospace Engineering, Xiamen University, Xiamen, 361000, China
| | - Peng Zheng
- School of Aerospace Engineering, Xiamen University, Xiamen, 361000, China
| | - Xinyu Lu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Lei Wang
- School of Aerospace Engineering, Xiamen University, Xiamen, 361000, China
| | - Bin Ren
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
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Kim K, Kashefi-Kheyrabadi L, Joung Y, Kim K, Dang H, Chavan SG, Lee MH, Choo J. Recent advances in sensitive surface-enhanced Raman scattering-based lateral flow assay platforms for point-of-care diagnostics of infectious diseases. SENSORS AND ACTUATORS. B, CHEMICAL 2021; 329:129214. [PMID: 36568647 PMCID: PMC9759493 DOI: 10.1016/j.snb.2020.129214] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 11/03/2020] [Accepted: 11/05/2020] [Indexed: 05/03/2023]
Abstract
This review reports the recent advances in surface-enhanced Raman scattering (SERS)-based lateral flow assay (LFA) platforms for the diagnosis of infectious diseases. As observed through the recent infection outbreaks of COVID-19 worldwide, a timely diagnosis of the disease is critical for preventing the spread of a disease and to ensure epidemic preparedness. In this regard, an innovative point-of-care diagnostic method is essential. Recently, SERS-based assay platforms have received increasing attention in medical communities owing to their high sensitivity and multiplex detection capability. In contrast, LFAs provide a user-friendly and easily accessible sensing platform. Thus, the combination of LFAs with a SERS detection system provides a new diagnostic modality for accurate and rapid diagnoses of infectious diseases. In this context, we briefly discuss the recent application of LFA platforms for the POC diagnosis of SARS-CoV-2. Thereafter, we focus on the recent advances in SERS-based LFA platforms for the early diagnosis of infectious diseases and their applicability for the rapid diagnosis of SARS-CoV-2. Finally, the key issues that need to be addressed to accelerate the clinical translation of SERS-based LFA platforms from the research laboratory to the bedside are discussed.
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Key Words
- AuNPs, gold nanoparticles
- BA, bacillary angiomatosis
- CRISPR, Clustered Regularly Interspaced Short Palindromic Repeat
- HIV, human immunodeficiency virus
- IFA, indirect immunofluorescence assay
- IgG, immunoglobulin G
- IgM, immunoglobulin M
- In vitro diagnostics (IVD)
- Infectious disease
- KSHV, Kaposi’s sarcoma herpes virus
- LFA, lateral flow assay
- Lateral flow assay (LFA)
- NC, nitrocellulose
- NS1, nonstructural protein 1
- POC, point-of-care
- PRV, pseudorabies virus
- Point-of-care (POC)
- RT-PCR, real-time polymerase chain reaction
- SARS-CoV-2
- SARS-CoV-2, severe acute respiratory syndrome-coronavirus-2
- SEB, staphylococcal enterotoxin
- SERS, surface-enhanced Raman scattering
- Si-AuNPs, silica-encapsulated AuNPs
- Surface-enhanced Raman scattering (SERS)
- crRNAs, CRISPR RNAs
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Affiliation(s)
- Kihyun Kim
- Department of Chemistry, Chung-Ang University, Seoul, 06974, South Korea
| | | | - Younju Joung
- Department of Chemistry, Chung-Ang University, Seoul, 06974, South Korea
| | - Kyeongnyeon Kim
- Department of Chemistry, Chung-Ang University, Seoul, 06974, South Korea
| | - Hajun Dang
- Department of Chemistry, Chung-Ang University, Seoul, 06974, South Korea
| | - Sachin Ganpat Chavan
- School of Integrative Engineering, Chung-Ang University, Seoul, 06974, South Korea
| | - Min-Ho Lee
- School of Integrative Engineering, Chung-Ang University, Seoul, 06974, South Korea
| | - Jaebum Choo
- Department of Chemistry, Chung-Ang University, Seoul, 06974, South Korea
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Venugopalan J, Tong L, Hassanzadeh HR, Wang MD. Multimodal deep learning models for early detection of Alzheimer's disease stage. Sci Rep 2021; 11:3254. [PMID: 33547343 PMCID: PMC7864942 DOI: 10.1038/s41598-020-74399-w] [Citation(s) in RCA: 178] [Impact Index Per Article: 44.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 01/22/2020] [Indexed: 02/06/2023] Open
Abstract
Most current Alzheimer's disease (AD) and mild cognitive disorders (MCI) studies use single data modality to make predictions such as AD stages. The fusion of multiple data modalities can provide a holistic view of AD staging analysis. Thus, we use deep learning (DL) to integrally analyze imaging (magnetic resonance imaging (MRI)), genetic (single nucleotide polymorphisms (SNPs)), and clinical test data to classify patients into AD, MCI, and controls (CN). We use stacked denoising auto-encoders to extract features from clinical and genetic data, and use 3D-convolutional neural networks (CNNs) for imaging data. We also develop a novel data interpretation method to identify top-performing features learned by the deep-models with clustering and perturbation analysis. Using Alzheimer's disease neuroimaging initiative (ADNI) dataset, we demonstrate that deep models outperform shallow models, including support vector machines, decision trees, random forests, and k-nearest neighbors. In addition, we demonstrate that integrating multi-modality data outperforms single modality models in terms of accuracy, precision, recall, and meanF1 scores. Our models have identified hippocampus, amygdala brain areas, and the Rey Auditory Verbal Learning Test (RAVLT) as top distinguished features, which are consistent with the known AD literature.
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Affiliation(s)
- Janani Venugopalan
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Li Tong
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Hamid Reza Hassanzadeh
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - May D Wang
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
- Winship Cancer Institute, Parker H. Petit Institute for Bioengineering and Biosciences, Institute of People and Technology, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
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29
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Fang J, Swain A, Unni R, Zheng Y. Decoding Optical Data with Machine Learning. LASER & PHOTONICS REVIEWS 2021; 15:2000422. [PMID: 34539925 PMCID: PMC8443240 DOI: 10.1002/lpor.202000422] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Indexed: 05/24/2023]
Abstract
Optical spectroscopy and imaging techniques play important roles in many fields such as disease diagnosis, biological study, information technology, optical science, and materials science. Over the past decade, machine learning (ML) has proved promising in decoding complex data, enabling rapid and accurate analysis of optical spectra and images. This review aims to shed light on various ML algorithms for optical data analysis with a focus on their applications in a wide range of fields. The goal of this work is to sketch the validity of ML-based optical data decoding. The review concludes with an outlook on unaddressed problems and opportunities in this emerging subject that interfaces optics, data science and ML.
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Affiliation(s)
- Jie Fang
- Walker Department of Mechanical Engineering and Texas Materials Institute, The University of Texas at Austin, Austin, TX 78712, USA
| | - Anand Swain
- Walker Department of Mechanical Engineering and Texas Materials Institute, The University of Texas at Austin, Austin, TX 78712, USA
| | - Rohit Unni
- Walker Department of Mechanical Engineering and Texas Materials Institute, The University of Texas at Austin, Austin, TX 78712, USA
| | - Yuebing Zheng
- Walker Department of Mechanical Engineering and Texas Materials Institute, The University of Texas at Austin, Austin, TX 78712, USA
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30
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Ramya AN, Arya JS, Madhukrishnan M, Shamjith S, Vidyalekshmi MS, Maiti KK. Raman Imaging: An Impending Approach Towards Cancer Diagnosis. Chem Asian J 2021; 16:409-422. [PMID: 33443291 DOI: 10.1002/asia.202001340] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 01/11/2021] [Indexed: 12/18/2022]
Abstract
In accordance with the recent studies, Raman spectroscopy is well experimented as a highly sensitive analytical and imaging technique in biomedical research, mainly for various disease diagnosis including cancer. In comparison with other imaging modalities, Raman spectroscopy facilitate numerous assistances owing to its low background signal, immense spatial resolution, high chemical specificity, multiplexing capability, excellent photo stability and non-invasive detection capability. In cancer diagnosis Raman imaging intervened as a promising investigative tool to provide molecular level information to differentiate the cancerous vs non-cancerous cells, tissues and even in body fluids. Anciently, spontaneous Raman scattering is very feeble due to its low signal intensity and long acquisition time but new advanced techniques like coherent Raman scattering (CRS) and surface enhanced Raman scattering (SERS) gradually superseded these issues. So, the present review focuses on the recent developments and applications of Raman spectroscopy-based imaging techniques for cancer diagnosis.
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Affiliation(s)
- Adukkadan N Ramya
- Chemical Sciences and Technology Division (CSTD), CSIR-National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Thiruvananthapuram, 695019, Kerala, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Jayadev S Arya
- Chemical Sciences and Technology Division (CSTD), CSIR-National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Thiruvananthapuram, 695019, Kerala, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Murali Madhukrishnan
- Chemical Sciences and Technology Division (CSTD), CSIR-National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Thiruvananthapuram, 695019, Kerala, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Shanmughan Shamjith
- Chemical Sciences and Technology Division (CSTD), CSIR-National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Thiruvananthapuram, 695019, Kerala, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Murukan S Vidyalekshmi
- Chemical Sciences and Technology Division (CSTD), CSIR-National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Thiruvananthapuram, 695019, Kerala, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Kaustabh K Maiti
- Chemical Sciences and Technology Division (CSTD), CSIR-National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Thiruvananthapuram, 695019, Kerala, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
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31
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Shirbandi K, Khalafi M, Mirza-Aghazadeh-Attari M, Tahmasbi M, Kiani Shahvandi H, Javanmardi P, Rahim F. Accuracy of deep learning model-assisted amyloid positron emission tomography scan in predicting Alzheimer's disease: A Systematic Review and meta-analysis. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100710] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
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32
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Combined application of MRS and DWI can effectively predict cell proliferation and assess the grade of glioma: A prospective study. J Clin Neurosci 2020; 83:56-63. [PMID: 33334663 DOI: 10.1016/j.jocn.2020.11.030] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Revised: 10/06/2020] [Accepted: 11/23/2020] [Indexed: 11/23/2022]
Abstract
In order to assess combined application of MRS and DWI for prediction cell proliferation and grade diagnosis of glioma, We prospectively collected the Cho/Cr, Cho/NAA, Cr/NAA of MRS and tumor parenchyma ADC (ADCT), contralateral mirror brain tissue ADC (ADCH), rADC (rADC = ADCT/ADCH). According to postoperative pathology, the patients were divided into two groups: LGG group and HGG group, compared differences of age, gender, Ki67, MRS, DWI between two groups. Next, we analyzed the correlation between MRS, DWI and Ki67. On this basis, the sensitivity and specificity of MRS, DWI and MRS combined with DWI (MRS + DWI) in diagnosis of glioma grade were evaluated. The differences of Ki67, Cho/Cr, Cho/NAA, Cr/NAA, ADCT, rADC between LGG group and HGG group were statistically significant (p = 0.000, 0.000, 0.000, 0.008, 0.000, and 0.000 respectively). From ROC curve, area under the curve (AUC), sensitivity and specificity of Cho/Cr, Cho/NAA, Cr/NAA, ADCT, rADC, PRE (MRS + DWI) were (0.901, 86.7%, 85.7%), (0.876, 80.0%, 82.1%), (0.704, 63.3%, 71.4%), (0.862, 82.1%, 83.3%), (0.820, 75.0%, 76.7%), (0.920, 86.7%, 89.3%), respectively. Fisher's linear discriminant functions results suggest: Y1 = -20.447 + 3.46•X1 + 17.141•X2 (LGG), Y2 = -19.415 + 4.828•X1 + 14.543•X2 (HGG). Our study suggested that MRS and DWI can effectively predict cell proliferation preoperative. MRS combined with DWI can further improve sensitivity and specificity in assessing the grade of glioma.
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33
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Sun J, Wang L, Liu Q, Tárnok A, Su X. Deep learning-based light scattering microfluidic cytometry for label-free acute lymphocytic leukemia classification. BIOMEDICAL OPTICS EXPRESS 2020; 11:6674-6686. [PMID: 33282516 PMCID: PMC7687967 DOI: 10.1364/boe.405557] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 10/14/2020] [Accepted: 10/14/2020] [Indexed: 05/27/2023]
Abstract
The subtyping of Acute lymphocytic leukemia (ALL) is important for proper treatment strategies and prognosis. Conventional methods for manual blood and bone marrow testing are time-consuming and labor-intensive, while recent flow cytometric immunophenotyping has the limitations such as high cost. Here we develop the deep learning-based light scattering imaging flow cytometry for label-free classification of ALL. The single ALL cells confined in three dimensional (3D) hydrodynamically focused stream are excited by light sheet. Our label-free microfluidic cytometry obtains big-data two dimensional (2D) light scattering patterns from single ALL cells of B/T subtypes. A deep learning framework named Inception V3-SIFT (Scale invariant feature transform)-Scattering Net (ISSC-Net) is developed, which can perform high-precision classification of T-ALL and B-ALL cell line cells with an accuracy of 0.993 ± 0.003. Our deep learning-based 2D light scattering flow cytometry is promising for automatic and accurate subtyping of un-stained ALL.
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Affiliation(s)
- Jing Sun
- School of Microelectronics, Shandong University, Jinan, China
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Lan Wang
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Qiao Liu
- Key Laboratory of Experimental Teratology (Ministry of Education); Department of Molecular Medicine and Genetics, School of Basic Medicine Sciences, Shandong University, Jinan, China
| | - Attila Tárnok
- Department of Therapy Validation, Fraunhofer Institute for Cell Therapy and Immunology (IZI), Leipzig, Germany
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany
| | - Xuantao Su
- School of Microelectronics, Shandong University, Jinan, China
- Advanced Medical Research Institute, Shandong University, Jinan, China
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34
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Heng HPS, Shu C, Zheng W, Lin K, Huang Z. Advances in real‐time fiber‐optic Raman spectroscopy for early cancer diagnosis: Pushing the frontier into clinical endoscopic applications. TRANSLATIONAL BIOPHOTONICS 2020. [DOI: 10.1002/tbio.202000018] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Affiliation(s)
- Howard Peng Sin Heng
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering National University of Singapore Singapore Singapore
- NUS Graduate School for Integrative Sciences and Engineering National University of Singapore Singapore Singapore
| | - Chi Shu
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering National University of Singapore Singapore Singapore
| | - Wei Zheng
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering National University of Singapore Singapore Singapore
| | - Kan Lin
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering National University of Singapore Singapore Singapore
| | - Zhiwei Huang
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering National University of Singapore Singapore Singapore
- NUS Graduate School for Integrative Sciences and Engineering National University of Singapore Singapore Singapore
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35
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Brozek-Pluska B. Statistics assisted analysis of Raman spectra and imaging of human colon cell lines – Label free, spectroscopic diagnostics of colorectal cancer. J Mol Struct 2020. [DOI: 10.1016/j.molstruc.2020.128524] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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36
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Yu S, Li H, Li X, Fu YV, Liu F. Classification of pathogens by Raman spectroscopy combined with generative adversarial networks. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 726:138477. [PMID: 32315848 DOI: 10.1016/j.scitotenv.2020.138477] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 04/02/2020] [Accepted: 04/03/2020] [Indexed: 06/11/2023]
Abstract
Rapid identification of marine pathogens is very important in marine ecology. Artificial intelligence combined with Raman spectroscopy is a promising choice for identifying marine pathogens due to its rapidity and efficiency. However, considering the cost of sample collection and the challenging nature of the experimental environment, only limited spectra are typically available to build a classification model, which hinders qualitative analysis. In this paper, we propose a novel method to classify marine pathogens by means of Raman spectroscopy combined with generative adversarial networks (GANs). Three marine strains, namely, Staphylococcus hominis, Vibrio alginolyticus, and Bacillus licheniformis, were cultured. Using Raman spectroscopy, we acquired 100 spectra of each strain, and we fitted them into GAN models for training. After 30,000 training iterations, the spectra generated by G were similar to the actual spectra, and D was used to test the accuracy of the spectra. Our results demonstrate that our method not only improves the accuracy of machine learning classification but also solves the problem of requiring a large amount of training data. Moreover, we have attempted to find potential identifying regions in the Raman spectra that can be used for reference in subsequent related work in this field. Therefore, this method has tremendous potential to be developed as a tool for pathogen identification.
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Affiliation(s)
- Shixiang Yu
- Key Laboratory of Coastal Biology and Biological Resources Utilization, CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, P. R. China; University of the Chinese Academy of Sciences, Beijing 100049, PR China
| | - Hanfei Li
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, PR China; University of the Chinese Academy of Sciences, Beijing 100049, PR China
| | - Xin Li
- Key Laboratory of Coastal Biology and Biological Resources Utilization, CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, P. R. China; University of the Chinese Academy of Sciences, Beijing 100049, PR China
| | - Yu Vincent Fu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, PR China.
| | - Fanghua Liu
- Key Laboratory of Coastal Biology and Biological Resources Utilization, CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, P. R. China; National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management, Guangdong Institute of Eco-environmental Science & Technology, Guangdong Academy of Sciences, Guangzhou 510650, PR China; Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, PR China.
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37
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Pradhan P, Guo S, Ryabchykov O, Popp J, Bocklitz TW. Deep learning a boon for biophotonics? JOURNAL OF BIOPHOTONICS 2020; 13:e201960186. [PMID: 32167235 DOI: 10.1002/jbio.201960186] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 02/22/2020] [Accepted: 03/10/2020] [Indexed: 06/10/2023]
Abstract
This review covers original articles using deep learning in the biophotonic field published in the last years. In these years deep learning, which is a subset of machine learning mostly based on artificial neural network geometries, was applied to a number of biophotonic tasks and has achieved state-of-the-art performances. Therefore, deep learning in the biophotonic field is rapidly growing and it will be utilized in the next years to obtain real-time biophotonic decision-making systems and to analyze biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement. Additionally, we discuss the potential use of deep learning for spectroscopic data including spectral data preprocessing and spectral classification. We conclude this review by addressing the potential applications and challenges of using deep learning for biophotonic data.
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Affiliation(s)
- Pranita Pradhan
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller-University, Jena, Germany
- Leibniz Institute of Photonic Technology (Leibniz-IPHT), Member of Leibniz Research Alliance 'Health Technologies', Jena, Germany
| | - Shuxia Guo
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller-University, Jena, Germany
- Leibniz Institute of Photonic Technology (Leibniz-IPHT), Member of Leibniz Research Alliance 'Health Technologies', Jena, Germany
| | - Oleg Ryabchykov
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller-University, Jena, Germany
- Leibniz Institute of Photonic Technology (Leibniz-IPHT), Member of Leibniz Research Alliance 'Health Technologies', Jena, Germany
| | - Juergen Popp
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller-University, Jena, Germany
- Leibniz Institute of Photonic Technology (Leibniz-IPHT), Member of Leibniz Research Alliance 'Health Technologies', Jena, Germany
| | - Thomas W Bocklitz
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller-University, Jena, Germany
- Leibniz Institute of Photonic Technology (Leibniz-IPHT), Member of Leibniz Research Alliance 'Health Technologies', Jena, Germany
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38
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Chrabaszcz K, Meyer T, Bae H, Schmitt M, Jasztal A, Smeda M, Stojak M, Popp J, Malek K, Marzec KM. Comparison of standard and HD FT-IR with multimodal CARS/TPEF/SHG/FLIMS imaging in the detection of the early stage of pulmonary metastasis of murine breast cancer. Analyst 2020; 145:4982-4990. [DOI: 10.1039/d0an00762e] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The comparison of the potential of FT-IR in standard and high definition modes with multimodal CARS/TPEF/SHG/FLIMS imaging for detection of the early stage of pulmonary metastasis of murine breast cancer is presented.
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Affiliation(s)
- Karolina Chrabaszcz
- Faculty of Chemistry
- Jagiellonian University
- 30-387 Krakow
- Poland
- Jagiellonian Centre for Experimental Therapeutics
| | - Tobias Meyer
- Leibniz-Institute of Photonic Technology e.V
- Member of Leibniz Health Technologies
- 07745 Jena
- Germany
- Institute of Physical Chemistry and Abbe Center of Photonics
| | - Hyeonsoo Bae
- Institute of Physical Chemistry and Abbe Center of Photonics
- Friedrich-Schiller-University
- 07745 Jena
- Germany
| | - Michael Schmitt
- Institute of Physical Chemistry and Abbe Center of Photonics
- Friedrich-Schiller-University
- 07745 Jena
- Germany
| | - Agnieszka Jasztal
- Jagiellonian Centre for Experimental Therapeutics
- Jagiellonian University
- 30-384 Krakow
- Poland
| | - Marta Smeda
- Jagiellonian Centre for Experimental Therapeutics
- Jagiellonian University
- 30-384 Krakow
- Poland
| | - Marta Stojak
- Jagiellonian Centre for Experimental Therapeutics
- Jagiellonian University
- 30-384 Krakow
- Poland
| | - Jürgen Popp
- Leibniz-Institute of Photonic Technology e.V
- Member of Leibniz Health Technologies
- 07745 Jena
- Germany
- Institute of Physical Chemistry and Abbe Center of Photonics
| | - Kamilla Malek
- Faculty of Chemistry
- Jagiellonian University
- 30-387 Krakow
- Poland
- Jagiellonian Centre for Experimental Therapeutics
| | - Katarzyna M. Marzec
- Jagiellonian Centre for Experimental Therapeutics
- Jagiellonian University
- 30-384 Krakow
- Poland
- Centre for Medical Genomics OMICRON
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Bocklitz T, Silge A, Bae H, Rodewald M, Legesse FB, Meyer T, Popp J. Non-invasive Imaging Techniques: From Histology to In Vivo Imaging : Chapter of Imaging in Oncology. Recent Results Cancer Res 2020; 216:795-812. [PMID: 32594407 DOI: 10.1007/978-3-030-42618-7_25] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
In this chapter, we will introduce and review molecular-sensitive imaging techniques, which close the gap between ex vivo and in vivo analysis. In detail, we will introduce spontaneous Raman spectral imaging, coherent anti-Stokes Raman scattering (CARS), stimulated Raman scattering (SRS), second-harmonic generation (SHG) and third-harmonic generation (THG), two-photon excited fluorescence (TPEF), and fluorescence lifetime imaging (FLIM). After reviewing these imaging techniques, we shortly introduce chemometric methods and machine learning techniques, which are needed to use these imaging techniques in diagnostic applications.
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Affiliation(s)
- Thomas Bocklitz
- University of Jena, IPC, Helmholtzweg 4, 07743, Jena, Germany.
| | - Anja Silge
- University of Jena, IPC, Helmholtzweg 4, 07743, Jena, Germany
| | - Hyeonsoo Bae
- University of Jena, IPC, Helmholtzweg 4, 07743, Jena, Germany
| | - Marko Rodewald
- University of Jena, IPC, Helmholtzweg 4, 07743, Jena, Germany
| | | | - Tobias Meyer
- University of Jena, IPC, Helmholtzweg 4, 07743, Jena, Germany
| | - Jürgen Popp
- University of Jena, IPC, Helmholtzweg 4, 07743, Jena, Germany.
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40
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Rangan S, Schulze HG, Vardaki MZ, Blades MW, Piret JM, Turner RFB. Applications of Raman spectroscopy in the development of cell therapies: state of the art and future perspectives. Analyst 2020; 145:2070-2105. [DOI: 10.1039/c9an01811e] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
This comprehensive review article discusses current and future perspectives of Raman spectroscopy-based analyses of cell therapy processes and products.
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Affiliation(s)
- Shreyas Rangan
- Michael Smith Laboratories
- The University of British Columbia
- Vancouver
- Canada
- School of Biomedical Engineering
| | - H. Georg Schulze
- Michael Smith Laboratories
- The University of British Columbia
- Vancouver
- Canada
| | - Martha Z. Vardaki
- Michael Smith Laboratories
- The University of British Columbia
- Vancouver
- Canada
| | - Michael W. Blades
- Department of Chemistry
- The University of British Columbia
- Vancouver
- Canada
| | - James M. Piret
- Michael Smith Laboratories
- The University of British Columbia
- Vancouver
- Canada
- School of Biomedical Engineering
| | - Robin F. B. Turner
- Michael Smith Laboratories
- The University of British Columbia
- Vancouver
- Canada
- Department of Chemistry
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Wang R, He Y, Yao C, Wang S, Xue Y, Zhang Z, Wang J, Liu X. Classification and Segmentation of Hyperspectral Data of Hepatocellular Carcinoma Samples Using 1-D Convolutional Neural Network. Cytometry A 2020; 97:31-38. [PMID: 31403260 DOI: 10.1002/cyto.a.23871] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 07/16/2019] [Accepted: 07/19/2019] [Indexed: 12/24/2022]
Abstract
Pathological diagnosis plays an important role in the diagnosis and treatment of hepatocellular carcinoma (HCC). The traditional method of pathological diagnosis of most cancers requires freezing, slicing, hematoxylin and eosin staining, and manual analysis, limiting the speed of the diagnosis process. In this study, we designed a one-dimensional convolutional neural network to classify the hyperspectral data of HCC sample slices acquired by our hyperspectral imaging system. A weighted loss function was employed to promote the performance of the model. The proposed method allows us to accelerate the diagnosis process of identifying tumor tissues. Our deep learning model achieved good performance on our data set with sensitivity, specificity, and area under receiver operating characteristic curve of 0.871, 0.888, and 0.950, respectively. Meanwhile, our deep learning model outperformed the other machine learning methods assessed on our data set. The proposed method is a potential tool for the label-free and real-time pathologic diagnosis. © 2019 International Society for Advancement of Cytometry.
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Affiliation(s)
- Rendong Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Yida He
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Cuiping Yao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Sijia Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Yuan Xue
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Zhenxi Zhang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Jing Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Xiaolong Liu
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, People's Republic of China
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Smith JT, Yao R, Sinsuebphon N, Rudkouskaya A, Un N, Mazurkiewicz J, Barroso M, Yan P, Intes X. Fast fit-free analysis of fluorescence lifetime imaging via deep learning. Proc Natl Acad Sci U S A 2019; 116:24019-24030. [PMID: 31719196 PMCID: PMC6883809 DOI: 10.1073/pnas.1912707116] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Fluorescence lifetime imaging (FLI) provides unique quantitative information in biomedical and molecular biology studies but relies on complex data-fitting techniques to derive the quantities of interest. Herein, we propose a fit-free approach in FLI image formation that is based on deep learning (DL) to quantify fluorescence decays simultaneously over a whole image and at fast speeds. We report on a deep neural network (DNN) architecture, named fluorescence lifetime imaging network (FLI-Net) that is designed and trained for different classes of experiments, including visible FLI and near-infrared (NIR) FLI microscopy (FLIM) and NIR gated macroscopy FLI (MFLI). FLI-Net outputs quantitatively the spatially resolved lifetime-based parameters that are typically employed in the field. We validate the utility of the FLI-Net framework by performing quantitative microscopic and preclinical lifetime-based studies across the visible and NIR spectra, as well as across the 2 main data acquisition technologies. These results demonstrate that FLI-Net is well suited to accurately quantify complex fluorescence lifetimes in cells and, in real time, in intact animals without any parameter settings. Hence, FLI-Net paves the way to reproducible and quantitative lifetime studies at unprecedented speeds, for improved dissemination and impact of FLI in many important biomedical applications ranging from fundamental discoveries in molecular and cellular biology to clinical translation.
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Affiliation(s)
- Jason T Smith
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180;
| | - Ruoyang Yao
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180
| | - Nattawut Sinsuebphon
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180
| | - Alena Rudkouskaya
- Department of Molecular and Cellular Physiology, Albany Medical College, Albany, NY 12208
| | - Nathan Un
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180
| | - Joseph Mazurkiewicz
- Department of Neuroscience and Experimental Therapeutics, Albany Medical College, Albany, NY 12208
| | - Margarida Barroso
- Department of Molecular and Cellular Physiology, Albany Medical College, Albany, NY 12208
| | - Pingkun Yan
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180
| | - Xavier Intes
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180;
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Sunny S, Baby A, James BL, Balaji D, N. V. A, Rana MH, Gurpur P, Skandarajah A, D’Ambrosio M, Ramanjinappa RD, Mohan SP, Raghavan N, Kandasarma U, N. S, Raghavan S, Hedne N, Koch F, Fletcher DA, Selvam S, Kollegal M, N. PB, Ladic L, Suresh A, Pandya HJ, Kuriakose MA. A smart tele-cytology point-of-care platform for oral cancer screening. PLoS One 2019; 14:e0224885. [PMID: 31730638 PMCID: PMC6857853 DOI: 10.1371/journal.pone.0224885] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 10/23/2019] [Indexed: 12/14/2022] Open
Abstract
Early detection of oral cancer necessitates a minimally invasive, tissue-specific diagnostic tool that facilitates screening/surveillance. Brush biopsy, though minimally invasive, demands skilled cyto-pathologist expertise. In this study, we explored the clinical utility/efficacy of a tele-cytology system in combination with Artificial Neural Network (ANN) based risk-stratification model for early detection of oral potentially malignant (OPML)/malignant lesion. A portable, automated tablet-based tele-cytology platform capable of digitization of cytology slides was evaluated for its efficacy in the detection of OPML/malignant lesions (n = 82) in comparison with conventional cytology and histology. Then, an image pre-processing algorithm was established to segregate cells, ANN was trained with images (n = 11,981) and a risk-stratification model developed. The specificity, sensitivity and accuracy of platform/ stratification model were computed, and agreement was examined using Kappa statistics. The tele-cytology platform, Cellscope, showed an overall accuracy of 84–86% with no difference between tele-cytology and conventional cytology in detection of oral lesions (kappa, 0.67–0.72). However, OPML could be detected with low sensitivity (18%) in accordance with the limitations of conventional cytology. The integration of image processing and development of an ANN-based risk stratification model improved the detection sensitivity of malignant lesions (93%) and high grade OPML (73%), thereby increasing the overall accuracy by 30%. Tele-cytology integrated with the risk stratification model, a novel strategy established in this study, can be an invaluable Point-of-Care (PoC) tool for early detection/screening in oral cancer. This study hence establishes the applicability of tele-cytology for accurate, remote diagnosis and use of automated ANN-based analysis in improving its efficacy.
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Affiliation(s)
- Sumsum Sunny
- Head and Neck Oncology, Mazumdar Shaw Medical Centre, NH Health city, Bangalore, India
- Integrated Head and Neck Oncology Program (DSRG-5), Mazumdar Shaw Medical Foundation, NH Health city, Bangalore, India
- Manipal Academy of Higher Education, Manipal, Karnataka, India
- Biomedical and Electronic (10-10) Engineering Systems Laboratory, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | - Arun Baby
- Biomedical and Electronic (10-10) Engineering Systems Laboratory, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | - Bonney Lee James
- Integrated Head and Neck Oncology Program (DSRG-5), Mazumdar Shaw Medical Foundation, NH Health city, Bangalore, India
| | - Dev Balaji
- Biomedical and Electronic (10-10) Engineering Systems Laboratory, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | - Aparna N. V.
- Biomedical and Electronic (10-10) Engineering Systems Laboratory, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | - Maitreya H. Rana
- Biomedical and Electronic (10-10) Engineering Systems Laboratory, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | | | - Arunan Skandarajah
- Department of Bioengineering & Biophysics Program, University of California, Berkeley, California, United States of America
| | - Michael D’Ambrosio
- Department of Bioengineering & Biophysics Program, University of California, Berkeley, California, United States of America
| | | | - Sunil Paramel Mohan
- Department of Oral and Maxillofacial pathology, Sree Anjaneya Dental College, Kozhikode, Kerala, India
| | - Nisheena Raghavan
- Department of Pathology, Mazumdar Shaw Medical Centre, NH Health city, Bangalore, India
| | - Uma Kandasarma
- Department of Oral and Maxillofacial Pathology, KLE Society’s Institute of Dental Sciences, Bangalore, India
| | - Sangeetha N.
- Department of oral medicine and radiology, KLE Society’s Institute of Dental Sciences, Bangalore, India
| | - Subhasini Raghavan
- Department of oral medicine and radiology, KLE Society’s Institute of Dental Sciences, Bangalore, India
| | - Naveen Hedne
- Head and Neck Oncology, Mazumdar Shaw Medical Centre, NH Health city, Bangalore, India
| | - Felix Koch
- University of Mainz, 55099, Mainz, Germany
| | - Daniel A. Fletcher
- Department of Bioengineering & Biophysics Program, University of California, Berkeley, California, United States of America
| | - Sumithra Selvam
- Division of Epidemiology and Biostatistics, St. John’s Research Institute, St. John’s National Academy of Health Sciences, Bangalore, India
| | | | - Praveen Birur N.
- Head and Neck Oncology, Mazumdar Shaw Medical Centre, NH Health city, Bangalore, India
- Department of oral medicine and radiology, KLE Society’s Institute of Dental Sciences, Bangalore, India
| | - Lance Ladic
- Siemens Healthineers, Malvern, Pennsylvania, United States of America
| | - Amritha Suresh
- Head and Neck Oncology, Mazumdar Shaw Medical Centre, NH Health city, Bangalore, India
- Integrated Head and Neck Oncology Program (DSRG-5), Mazumdar Shaw Medical Foundation, NH Health city, Bangalore, India
| | - Hardik J. Pandya
- Biomedical and Electronic (10-10) Engineering Systems Laboratory, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
- * E-mail: (HJP); (MAK)
| | - Moni Abraham Kuriakose
- Head and Neck Oncology, Mazumdar Shaw Medical Centre, NH Health city, Bangalore, India
- Integrated Head and Neck Oncology Program (DSRG-5), Mazumdar Shaw Medical Foundation, NH Health city, Bangalore, India
- * E-mail: (HJP); (MAK)
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Aljakouch K, Hilal Z, Daho I, Schuler M, Krauß SD, Yosef HK, Dierks J, Mosig A, Gerwert K, El-Mashtoly SF. Fast and Noninvasive Diagnosis of Cervical Cancer by Coherent Anti-Stokes Raman Scattering. Anal Chem 2019; 91:13900-13906. [DOI: 10.1021/acs.analchem.9b03395] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Ralbovsky NM, Lednev IK. Raman spectroscopy and chemometrics: A potential universal method for diagnosing cancer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2019; 219:463-487. [PMID: 31075613 DOI: 10.1016/j.saa.2019.04.067] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 04/20/2019] [Accepted: 04/24/2019] [Indexed: 05/14/2023]
Abstract
Cancer is the second-leading cause of death worldwide. It affects an unfathomable number of people, with almost 16 million Americans currently living with it. While many cancers can be detected, current diagnostic efforts exhibit definite room for improvement. It is imperative that a person be diagnosed with cancer as early on in its progression as possible. An earlier diagnosis allows for the best treatment and intervention options available to be presented. Unfortunately, existing methods for diagnosing cancer can be expensive, invasive, inconclusive or inaccurate, and are not always made during initial stages of the disease. As such, there is a crucial unmet need to develop a singular universal method that is reliable, cost-effective, and non-invasive and can diagnose all forms of cancer early-on. Raman spectroscopy in combination with advanced statistical analysis is offered here as a potential solution for this need. This review covers recently published research in which Raman spectroscopy was used for the purpose of diagnosing cancer. The benefits and the risks of the methodology are presented; however, there is overwhelming evidence that suggests Raman spectroscopy is highly suitable for becoming the first universal method to be used for diagnosing cancer.
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Affiliation(s)
- Nicole M Ralbovsky
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, NY 12222, USA
| | - Igor K Lednev
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, NY 12222, USA.
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46
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Lin H, Wei C, Wang G, Chen H, Lin L, Ni M, Chen J, Zhuo S. Automated classification of hepatocellular carcinoma differentiation using multiphoton microscopy and deep learning. JOURNAL OF BIOPHOTONICS 2019; 12:e201800435. [PMID: 30868728 DOI: 10.1002/jbio.201800435] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 01/29/2019] [Accepted: 03/12/2019] [Indexed: 05/22/2023]
Abstract
In the case of hepatocellular carcinoma (HCC) samples, classification of differentiation is crucial for determining prognosis and treatment strategy decisions. However, a label-free and automated classification system for HCC grading has not been yet developed. Hence, in this study, we demonstrate the fusion of multiphoton microscopy and a deep-learning algorithm for classifying HCC differentiation to produce an innovative computer-aided diagnostic method. Convolutional neural networks based on the VGG-16 framework were trained using 217 combined two-photon excitation fluorescence and second-harmonic generation images; the resulting classification accuracy of the HCC differentiation grade was over 90%. Our results suggest that a combination of multiphoton microscopy and deep learning can realize label-free, automated methods for various tissues, diseases and other related classification problems.
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Affiliation(s)
- Hongxin Lin
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education and Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, P.R. China
| | - Chao Wei
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education and Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, P.R. China
| | - Guangxing Wang
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education and Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, P.R. China
| | - Hu Chen
- Department of Pathology, Fujian Medical University Union Hospital, Fuzhou, P.R. China
| | - Lisheng Lin
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education and Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, P.R. China
| | - Ming Ni
- School of Biological Sciences and Engineering, Yachay Tech University, San Miguel de Urcuquí, Ecuador
| | - Jianxin Chen
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education and Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, P.R. China
| | - Shuangmu Zhuo
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education and Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, P.R. China
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Scodellaro R, Bouzin M, Mingozzi F, D'Alfonso L, Granucci F, Collini M, Chirico G, Sironi L. Whole-Section Tumor Micro-Architecture Analysis by a Two-Dimensional Phasor-Based Approach Applied to Polarization-Dependent Second Harmonic Imaging. Front Oncol 2019; 9:527. [PMID: 31275857 PMCID: PMC6593899 DOI: 10.3389/fonc.2019.00527] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Accepted: 05/30/2019] [Indexed: 11/17/2022] Open
Abstract
Second Harmonic Generation (SHG) microscopy has gained much interest in the histopathology field since it allows label-free imaging of tissues simultaneously providing information on their morphology and on the collagen microarchitecture, thereby highlighting the onset of pathologies and diseases. A wide request of image analysis tools is growing, with the aim to increase the reliability of the analysis of the huge amount of acquired data and to assist pathologists in a user-independent way during their diagnosis. In this light, we exploit here a set of phasor-parameters that, coupled to a 2-dimensional phasor-based approach (μMAPPS, Microscopic Multiparametric Analysis by Phasor projection of Polarization-dependent SHG signal) and a clustering algorithm, allow to automatically recover different collagen microarchitectures in the tissues extracellular matrix. The collagen fibrils microscopic parameters (orientation and anisotropy) are analyzed at a mesoscopic level by quantifying their local spatial heterogeneity in histopathology sections (few mm in size) from two cancer xenografts in mice, in order to maximally discriminate different collagen organizations, allowing in this case to identify the tumor area with respect to the surrounding skin tissue. We show that the "fibril entropy" parameter, which describes the tissue order on a selected spatial scale, is the most effective in enlightening the tumor edges, opening the possibility of their automatic segmentation. Our method, therefore, combined with tissue morphology information, has the potential to become a support to standard histopathology in diseases diagnosis.
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Affiliation(s)
| | - Margaux Bouzin
- Physics Department, Università degli Studi di Milano-Bicocca, Milan, Italy
| | - Francesca Mingozzi
- Department of Biotechnology and Biosciences, Università degli Studi di Milano-Bicocca, Milan, Italy
| | - Laura D'Alfonso
- Physics Department, Università degli Studi di Milano-Bicocca, Milan, Italy
| | - Francesca Granucci
- Department of Biotechnology and Biosciences, Università degli Studi di Milano-Bicocca, Milan, Italy
| | - Maddalena Collini
- Physics Department, Università degli Studi di Milano-Bicocca, Milan, Italy
| | - Giuseppe Chirico
- Physics Department, Università degli Studi di Milano-Bicocca, Milan, Italy
| | - Laura Sironi
- Physics Department, Università degli Studi di Milano-Bicocca, Milan, Italy
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Signoroni A, Savardi M, Baronio A, Benini S. Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review. J Imaging 2019; 5:52. [PMID: 34460490 PMCID: PMC8320953 DOI: 10.3390/jimaging5050052] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 04/29/2019] [Accepted: 05/02/2019] [Indexed: 12/23/2022] Open
Abstract
Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial-spectral domain. This is fundamental in the driving sector of Remote Sensing where hyperspectral technology was born and has mostly developed, but it is perhaps even more true in the multitude of current and evolving application sectors that involve these imaging technologies. The present review develops on two fronts: on the one hand, it is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, we want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields other than Remote Sensing are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.
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Affiliation(s)
- Alberto Signoroni
- Information Engineering Department, University of Brescia, I25123 Brescia, Italy
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Abraham B, Nair MS. Computer-aided grading of prostate cancer from MRI images using Convolutional Neural Networks. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019. [DOI: 10.3233/jifs-169913] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Affiliation(s)
- Bejoy Abraham
- Department of Computer Science, University of Kerala, Kariavattom, Thiruvananthapuram 695581, Kerala, India
- Department of Computer Science and Engineering, College of Engineering Perumon, Kollam 691601, Kerala, India
| | - Madhu S. Nair
- Department of Computer Science, Cochin University of Science and Technology, Kochi 682022, Kerala, India
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50
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Enhancing Disease Diagnosis: Biomedical Applications of Surface-Enhanced Raman Scattering. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9061163] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Surface-enhanced Raman scattering (SERS) has recently gained increasing attention for the detection of trace quantities of biomolecules due to its excellent molecular specificity, ultrasensitivity, and quantitative multiplex ability. Specific single or multiple biomarkers in complex biological environments generate strong and distinct SERS spectral signals when they are in the vicinity of optically active nanoparticles (NPs). When multivariate chemometrics are applied to decipher underlying biomarker patterns, SERS provides qualitative and quantitative information on the inherent biochemical composition and properties that may be indicative of healthy or diseased states. Moreover, SERS allows for differentiation among many closely-related causative agents of diseases exhibiting similar symptoms to guide early prescription of appropriate, targeted and individualised therapeutics. This review provides an overview of recent progress made by the application of SERS in the diagnosis of cancers, microbial and respiratory infections. It is envisaged that recent technology development will help realise full benefits of SERS to gain deeper insights into the pathological pathways for various diseases at the molecular level.
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