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Chen Y, Zheng C, Hu F, Zhou T, Feng L, Xu G, Yi Z, Zhang X. Efficient two-step liver and tumour segmentation on abdominal CT via deep learning and a conditional random field. Comput Biol Med 2022; 150:106076. [PMID: 36137320 DOI: 10.1016/j.compbiomed.2022.106076] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/20/2022] [Accepted: 09/03/2022] [Indexed: 11/24/2022]
Abstract
Segmentation of the liver and tumours from computed tomography (CT) scans is an important task in hepatic surgical planning. Manual segmentation of the liver and tumours is a time-consuming and labour-intensive task; therefore, a fully automated method for performing this segmentation is particularly desired. An automatic two-step liver and tumour segmentation method is presented in this paper. A cascade framework is used during the segmentation process, and a fully connected conditional random field (CRF) method is used to refine the tumour segmentation result. First, the proposed fractal residual U-Net (FRA-UNet) is used to locate and initially segment the liver. Then, FRA-UNet is further used to predict liver tumours from the liver region of interest (ROI). Finally, a three-dimensional (3D) CRF is used to refine the tumour segmentation results. The improved fractal residual (FR) structure effectively retains more effective features for improving the segmentation performance of deeper networks, the improved deep residual block can utilise the feature information more effectively, and the 3D CRF method smooths the contours and avoids the tumour oversegmentation problem. FRA-UNet is tested on the Liver Tumour Segmentation challenge dataset (LiTS) and the 3D Image Reconstruction for Comparison of Algorithm Database dataset (3DIRCADb), achieving 97.13% and 97.18% Dice similarity coefficients (DSCs) for liver segmentation and 71.78% and 68.97% DSCs for tumour segmentation, respectively, outperforming most state-of-the-art networks.
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Affiliation(s)
- Ying Chen
- School of Software, Nanchang Hangkong University, Nanchang, 330063, China.
| | - Cheng Zheng
- School of Software, Nanchang Hangkong University, Nanchang, 330063, China.
| | - Fei Hu
- School of Software, Nanchang Hangkong University, Nanchang, 330063, China.
| | - Taohui Zhou
- School of Software, Nanchang Hangkong University, Nanchang, 330063, China.
| | - Longfeng Feng
- School of Software, Nanchang Hangkong University, Nanchang, 330063, China.
| | - Guohui Xu
- Department of Liver Neoplasms, Jiangxi Cancer Hospital, Nanchang, 330029, China.
| | - Zhen Yi
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, 330029, China.
| | - Xiang Zhang
- Wenzhou Data Management and Development Group Co.,Ltd, Wenzhou, Zhejiang, 325000, China.
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Liu L, Wang Y, Chang J, Zhang P, Liang G, Zhang H. LLRHNet: Multiple Lesions Segmentation Using Local-Long Range Features. Front Neuroinform 2022; 16:859973. [PMID: 35600503 PMCID: PMC9119082 DOI: 10.3389/fninf.2022.859973] [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: 01/22/2022] [Accepted: 03/22/2022] [Indexed: 11/13/2022] Open
Abstract
The encoder-decoder-based deep convolutional neural networks (CNNs) have made great improvements in medical image segmentation tasks. However, due to the inherent locality of convolution, CNNs generally are demonstrated to have limitations in obtaining features across layers and long-range features from the medical image. In this study, we develop a local-long range hybrid features network (LLRHNet), which inherits the merits of the iterative aggregation mechanism and the transformer technology, as a medical image segmentation model. LLRHNet adopts encoder-decoder architecture as the backbone which iteratively aggregates the projection and up-sampling to fuse local low-high resolution features across isolated layers. The transformer adopts the multi-head self-attention mechanism to extract long-range features from the tokenized image patches and fuses these features with the local-range features extracted by down-sampling operation in the backbone network. These hybrid features are used to assist the cascaded up-sampling operations to local the position of the target tissues. LLRHNet is evaluated on two multiple lesions medical image data sets, including a public liver-related segmentation data set (3DIRCADb) and an in-house stroke and white matter hyperintensity (SWMH) segmentation data set. Experimental results denote that LLRHNet achieves state-of-the-art performance on both data sets.
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Affiliation(s)
- Liangliang Liu
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, China
| | - Ying Wang
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, China
| | - Jing Chang
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, China
| | - Pei Zhang
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, China
| | - Gongbo Liang
- Department of Computer Science, Eastern Kentucky University, Richmond, KY, United States
| | - Hui Zhang
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, China
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Compound W-Net with Fully Accumulative Residual Connections for Liver Segmentation Using CT Images. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8501828. [PMID: 35186116 PMCID: PMC8850044 DOI: 10.1155/2022/8501828] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 12/07/2021] [Accepted: 01/06/2022] [Indexed: 11/21/2022]
Abstract
Computed tomography (CT) is a common modality for liver diagnosis, treatment, and follow-up process. Providing accurate liver segmentation using CT images is a crucial step towards those tasks. In this paper, we propose a stacked 2-U-Nets model with three different types of skip connections. The proposed connections work to recover the loss of high-level features on the convolutional path of the first U-Net due to the pooling and the loss of low-level features during the upsampling path of the first U-Net. The skip connections concatenate all the features that are generated at the same level from the previous paths to the inputs of the convolutional layers in both paths of the second U-Net in a densely connected manner. We implement two versions of the model with different number of filters at each level of each U-Net by maximising the Dice similarity between the predicted liver region and that of the ground truth. The proposed models were trained with 3Dircadb public dataset that were released for Sliver and 3D liver and tumour segmentation challenges during MICCAI 2007-2008 challenge. The experimental results show that the proposed model outperformed the original U-Net and 2-U-Nets variants, and is comparable to the state-of-the-art mU-Net, DC U-Net, and Cascaded UNET.
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Akakuru OU, Zhang Z, Iqbal MZ, Zhu C, Zhang Y, Wu A. Chemotherapeutic nanomaterials in tumor boundary delineation: Prospects for effective tumor treatment. Acta Pharm Sin B 2022; 12:2640-2657. [PMID: 35755279 PMCID: PMC9214073 DOI: 10.1016/j.apsb.2022.02.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 01/27/2022] [Accepted: 02/06/2022] [Indexed: 12/14/2022] Open
Abstract
Accurately delineating tumor boundaries is key to predicting survival rates of cancer patients and assessing response of tumor microenvironment to various therapeutic techniques such as chemotherapy and radiotherapy. This review discusses various strategies that have been deployed to accurately delineate tumor boundaries with particular emphasis on the potential of chemotherapeutic nanomaterials in tumor boundary delineation. It also compiles the types of tumors that have been successfully delineated by currently available strategies. Finally, the challenges that still abound in accurate tumor boundary delineation are presented alongside possible perspective strategies to either ameliorate or solve the problems. It is expected that the information communicated herein will form the first compendious baseline information on tumor boundary delineation with chemotherapeutic nanomaterials and provide useful insights into future possible paths to advancing current available tumor boundary delineation approaches to achieve efficacious tumor therapy.
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Affiliation(s)
- Ozioma Udochukwu Akakuru
- Cixi Institute of Biomedical Engineering, International Cooperation Base of Biomedical Materials Technology and Application, Chinese Academy of Sciences (CAS) Key Laboratory of Magnetic Materials and Devices, Zhejiang Engineering Research Center for Biomedical Materials, Ningbo Institute of Materials Technology and Engineering, CAS, Ningbo 315201, China
| | - Zhoujing Zhang
- School of Medicine, Southeast University, Nanjing 210009, China
| | - M. Zubair Iqbal
- Cixi Institute of Biomedical Engineering, International Cooperation Base of Biomedical Materials Technology and Application, Chinese Academy of Sciences (CAS) Key Laboratory of Magnetic Materials and Devices, Zhejiang Engineering Research Center for Biomedical Materials, Ningbo Institute of Materials Technology and Engineering, CAS, Ningbo 315201, China
- School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
| | - Chengjie Zhu
- Cixi Institute of Biomedical Engineering, International Cooperation Base of Biomedical Materials Technology and Application, Chinese Academy of Sciences (CAS) Key Laboratory of Magnetic Materials and Devices, Zhejiang Engineering Research Center for Biomedical Materials, Ningbo Institute of Materials Technology and Engineering, CAS, Ningbo 315201, China
| | - Yewei Zhang
- School of Medicine, Southeast University, Nanjing 210009, China
| | - Aiguo Wu
- Cixi Institute of Biomedical Engineering, International Cooperation Base of Biomedical Materials Technology and Application, Chinese Academy of Sciences (CAS) Key Laboratory of Magnetic Materials and Devices, Zhejiang Engineering Research Center for Biomedical Materials, Ningbo Institute of Materials Technology and Engineering, CAS, Ningbo 315201, China
- Corresponding author.
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Chen CI, Lu NH, Huang YH, Liu KY, Hsu SY, Matsushima A, Wang YM, Chen TB. Segmentation of liver tumors with abdominal computed tomography using fully convolutional networks. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022; 30:953-966. [PMID: 35754254 DOI: 10.3233/xst-221194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
BACKGROUND Dividing liver organs or lesions depicting on computed tomography (CT) images could be applied to help tumor staging and treatment. However, most existing image segmentation technologies use manual or semi-automatic analysis, making the analysis process costly and time-consuming. OBJECTIVE This research aims to develop and apply a deep learning network architecture to segment liver tumors automatically after fine tuning parameters. METHODS AND MATERIALS The medical imaging is obtained from the International Symposium on Biomedical Imaging (ISBI), which includes 3D abdominal CT scans of 131 patients diagnosed with liver tumors. From these CT scans, there are 7,190 2D CT images along with the labeled binary images. The labeled binary images are regarded as gold standard for evaluation of the segmented results by FCN (Fully Convolutional Network). The backbones of FCN are extracted from Xception, InceptionresNetv2, MobileNetv2, ResNet18, ResNet50 in this study. Meanwhile, the parameters including optimizers (SGDM and ADAM), size of epoch, and size of batch are investigated. CT images are randomly divided into training and testing sets using a ratio of 9:1. Several evaluation indices including Global Accuracy, Mean Accuracy, Mean IoU (Intersection over Union), Weighted IoU and Mean BF Score are applied to evaluate tumor segmentation results in the testing images. RESULTS The Global Accuracy, Mean Accuracy, Mean IoU, Weighted IoU, and Mean BF Scores are 0.999, 0.969, 0.954, 0.998, 0.962 using ResNet50 in FCN with optimizer SGDM, batch size 12, and epoch 9. It is important to fine tuning the parameters in FCN model. Top 20 FNC models enable to achieve higher tumor segmentation accuracy with Mean IoU over 0.900. The occurred frequency of InceptionresNetv2, MobileNetv2, ResNet18, ResNet50, and Xception are 9, 6, 3, 5, and 2 times. Therefore, the InceptionresNetv2 has higher performance than others. CONCLUSIONS This study develop and test an automated liver tumor segmentation model based on FCN. Study results demonstrate that many deep learning models including InceptionresNetv2, MobileNetv2, ResNet18, ResNet50, and Xception have high potential to segment liver tumors from CT images with accuracy exceeding 90%. However, it is still difficult to accurately segment tiny and small size tumors by FCN models.
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Affiliation(s)
- Chih-I Chen
- Division of Colon and Rectal Surgery, Department of Surgery, E-DA Hospital, Kaohsiung City, Taiwan
- Division of General Medicine Surgery, Department of Surgery, E-DA Hospital, Kaohsiung City, Taiwan
- School of Medicine, College of Medicine, I-Shou University, Kaohsiung City, Taiwan
- Department of Information Engineering, I-Shou University, Kaohsiung City, Taiwan
- The School of Chinese Medicine for Post Baccalaureate, I-Shou University, Kaohsiung City, Taiwan
| | - Nan-Han Lu
- Department of Pharmacy, Tajen University, Pingtung City, Taiwan
- Department of Radiology, E-DA Hospital, I-Shou University, Kaohsiung City, Taiwan
- Department of Medical Imaging and Radiological Science, I-Shou University, Kaohsiung City, Taiwan
| | - Yung-Hui Huang
- Department of Medical Imaging and Radiological Science, I-Shou University, Kaohsiung City, Taiwan
| | - Kuo-Ying Liu
- Department of Radiology, E-DA Hospital, I-Shou University, Kaohsiung City, Taiwan
| | - Shih-Yen Hsu
- Department of Information Engineering, I-Shou University, Kaohsiung City, Taiwan
| | - Akari Matsushima
- Department of Radiological Technology Faculty of Medical Technology, Teikyo University, Tokyo, Japan
| | - Yi-Ming Wang
- Department of Information Engineering, I-Shou University, Kaohsiung City, Taiwan
- Department of Critical Care Medicine, E-DA hospital, I-Shou University, Kaohsiung City, Taiwan
| | - Tai-Been Chen
- Department of Medical Imaging and Radiological Science, I-Shou University, Kaohsiung City, Taiwan
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
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Pham V, Nguyen H, Pham B, Nguyen T, Nguyen H. Robust Engineering-based Unified Biomedical Imaging Framework for Liver Tumor Segmentation. Curr Med Imaging 2022; 19:37-45. [PMID: 34348633 DOI: 10.2174/1573405617666210804151024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 06/02/2021] [Accepted: 06/12/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND Computer vision in general and semantic segmentation has experienced many achievements in recent years. Consequently, the emergence of medical imaging has provided new opportunities for conducting artificial intelligence research. Since cancer is the second-leading cause of death in the world, early-stage diagnosis is an essential process that directly slows down the development speed of cancer. METHODS Deep neural network-based methods are anticipated to reduce diagnosis time for pathologists. RESULTS In this research paper, an approach to liver tumor identification based on two types of medical images has been presented: computed tomography scans and whole-slide. It is constructed based on the improvement of U-Net and GLNet architectures. It also includes sub-modules that are combined with segmentation models to boost up the overall performance during inference phases. CONCLUSION Based on the experimental results, the proposed unified framework has been emerging to be used in the production environment.
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Affiliation(s)
- Vuong Pham
- Faculty of Information Technology, Sai Gon University, Ho Chi Minh City, Vietnam
| | - Hai Nguyen
- Faculty of Software Engineering, University of Information Technology, Ho Chi Minh City, Vietnam
- Vietnam National University, Ho Chi Minh City, Vietnam
| | - Bao Pham
- Faculty of Information Technology, Sai Gon University, Ho Chi Minh City, Vietnam
| | - Thien Nguyen
- VietNam Aviation Academy, Ho Chi Minh city, Vietnam
| | - Hien Nguyen
- Faculty of Computer Science, University of Information Technology, Ho Chi Minh City, Vietnam
- Vietnam National University, Ho Chi Minh City, Vietnam
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Kaur A, Kaur L, Singh A. GA-UNet: UNet-based framework for segmentation of 2D and 3D medical images applicable on heterogeneous datasets. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06134-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Alirr OI, Rahni AAA. Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters. J Digit Imaging 2021; 33:304-323. [PMID: 31428898 DOI: 10.1007/s10278-019-00262-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Preoperative planning for liver surgical treatments is an essential planning tool that aids in reducing the risks of surgical resection. Based on the computed tomography (CT) images, the resection can be planned before the actual tumour resection surgery. The computer-aided system provides an overview of the spatial relationships of the liver organ and its internal structures, tumours, and vasculature. It also allows for an accurate calculation of the remaining liver volume after resection. The aim of this paper was to review the main stages of the computer-aided system that helps to evaluate the risk of resection during liver cancer surgical treatments. The computer-aided system assists with surgical planning by enabling physicians to get volumetric measurements and visualise the liver, tumours, and surrounding vasculature. In this paper, it is concluded that for accurate planning of tumour resections, the liver organ and its internal structures should be segmented to understand the clear spatial relationship between them, thus allowing for a safer resection. This paper presents the main proposed segmentation techniques for each stage in the computer-aided system, namely the liver organ, tumours, and vessels. From the reviewed methods, it has been found that instead of relying on a single specific technique, a combination of a group of techniques would give more accurate segmentation results. The extracted masks from the segmentation algorithms are fused together to give the surgeons the 3D visualisation tool to study the spatial relationships of the liver and to calculate the required resection planning parameters.
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Affiliation(s)
- Omar Ibrahim Alirr
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia.
| | - Ashrani Aizzuddin Abd Rahni
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia
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Yang Z, Zhao YQ, Liao M, Di SH, Zeng YZ. Semi-automatic liver tumor segmentation with adaptive region growing and graph cuts. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102670] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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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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Akhtar Y, Dakua SP, Abdalla A, Aboumarzouk OM, Ansari MY, Abinahed J, Elakkad MSM, Al-Ansari A. Risk Assessment of Computer-aided Diagnostic Software for Hepatic Resection. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021. [DOI: 10.1109/trpms.2021.3071148] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Yusuf Akhtar
- Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India
| | | | | | | | | | - Julien Abinahed
- Hamad General Hospital, Hamad Medical Corporation, Doha, Qatar
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Liu L, Wu FX, Wang YP, Wang J. Multi-Receptive-Field CNN for Semantic Segmentation of Medical Images. IEEE J Biomed Health Inform 2020; 24:3215-3225. [DOI: 10.1109/jbhi.2020.3016306] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Siriapisith T, Kusakunniran W, Haddawy P. Pyramid graph cut: Integrating intensity and gradient information for grayscale medical image segmentation. Comput Biol Med 2020; 126:103997. [PMID: 32987203 DOI: 10.1016/j.compbiomed.2020.103997] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 08/30/2020] [Accepted: 08/30/2020] [Indexed: 11/17/2022]
Abstract
Segmentation of grayscale medical images is challenging because of the similarity of pixel intensities and poor gradient strength between adjacent regions. The existing image segmentation approaches based on either intensity or gradient information alone often fail to produce accurate segmentation results. Previous approaches in the literature have approached the problem by embedded or sequential integration of different information types to improve the performance of the image segmentation on specific tasks. However, an effective combination or integration of such information is difficult to implement and not sufficiently generic for closely related tasks. Integration of the two information sources in a single graph structure is a potentially more effective way to solve the problem. In this paper we introduce a novel technique for grayscale medical image segmentation called pyramid graph cut, which combines intensity and gradient sources of information in a pyramid-shaped graph structure using a single source node and multiple sink nodes. The source node, which is the top of the pyramid graph, embeds intensity information into its linked edges. The sink nodes, which are the base of the pyramid graph, embed gradient information into their linked edges. The min-cut uses intensity information and gradient information, depending on which one is more useful or has a higher influence in each cutting location of each iteration. The experimental results demonstrate the effectiveness of the proposed method over intensity-based segmentation alone (i.e. Gaussian mixture model) and gradient-based segmentation alone (i.e. distance regularized level set evolution) on grayscale medical image datasets, including the public 3DIRCADb-01 dataset. The proposed method archives excellent segmentation results on the sample CT of abdominal aortic aneurysm, MRI of liver tumor and US of liver tumor, with dice scores of 90.49±5.23%, 88.86±11.77%, 90.68±2.45%, respectively.
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Affiliation(s)
- Thanongchai Siriapisith
- Department Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand.
| | - Worapan Kusakunniran
- Faculty of Information and Communication Technology, Mahidol University, Nakhonpathom, 73170, Thailand
| | - Peter Haddawy
- Faculty of Information and Communication Technology, Mahidol University, Nakhonpathom, 73170, Thailand; Bremen Spatial Cognition Center, University of Bremen, Bremen, Germany
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Ramamurthy M, Krishnamurthi I, Vimal S, Robinson YH. Deep learning based genome analysis and NGS-RNA LL identification with a novel hybrid model. Biosystems 2020; 197:104211. [PMID: 32795485 DOI: 10.1016/j.biosystems.2020.104211] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Revised: 07/08/2020] [Accepted: 07/16/2020] [Indexed: 12/18/2022]
Abstract
The conventional image segmentation techniques have a lot of issues with highest computational cost and low level accuracy for medical image diagnosis and genome analysis. The deep learning based optimization models utilize to predict the liver cancer with RNA genome using CT images and the prediction of genome classification with NGS is a higher probable in recent medical disease classification. This paper proposes a hybrid deep learning technique constructs with SegNet, MultiResUNet, and Krill Herd optimization (KHO) algorithm to perform the extraction of the liver lesions and RNA sequencing that the optimization techniques used into the deep learning method. The proposed technique implements the SegNet for segregating the liver with genome from the CT scan; the MultiResUNet is constructed to perform the extractions of liver lesions. The KHO algorithm is combined with the deep learning approaches for tuning the hyper parameters to every Convolutional neural network model and enhances the segmentation process which may elaborately identifies the sequence that causes the liver classification disease. The proposed technique is compared with the related techniques on liver lesion classification (LL) for NGS in genome. The performance results show that the proposed technique is better to other algorithms on various performance metrics.
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Affiliation(s)
- Madhumitha Ramamurthy
- Department of Information Technology, Karpagam College of Engineering, Coimbatore, TamilNadu, India.
| | - Ilango Krishnamurthi
- Department of CSE, Sri Krishna College of Engineering and Technology, Coimbatore, TamilNadu, India.
| | - S Vimal
- Department of Information Technology, National Engineering College, Kovilpatti, Tamil Nadu, India.
| | - Y Harold Robinson
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.
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16
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Machine-Learning Based Hybrid-Feature Analysis for Liver Cancer Classification Using Fused (MR and CT) Images. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10093134] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The purpose of this research is to demonstrate the ability of machine-learning (ML) methods for liver cancer classification using a fused dataset of two-dimensional (2D) computed tomography (CT) scans and magnetic resonance imaging (MRI). Datasets of benign (hepatocellular adenoma, hemangioma, cyst) and malignant (hepatocellular carcinoma, hepatoblastoma, metastasis) liver cancer were acquired at Bahawal Victoria Hospital (BVH), Bahawalpur, Pakistan. The final dataset was generated by fusion of 1200 (100 × 6 × 2) MR and CT-scan images, 200 (100 MRI and 100 CT-scan) images size 512 × 512 for each class of cancer. The acquired dataset was preprocessed by employing the Gabor filters to reduce the noise and taking an automated region of interest (ROIs) using an Otsu thresholding-based segmentation approach. The preprocessed dataset was used to acquire 254 hybrid-feature data for each ROI, which is the combination of the histogram, wavelet, co-occurrence, and run-length features, while 10 optimized hybrid features were selected by employing (probability of error plus average correlation) feature selection technique. For classification, we deployed this optimized hybrid-feature dataset to four ML classifiers: multilayer perceptron (MLP), support vector machine (SVM), random forest (RF), and J48, using a ten fold cross-validation method. MLP showed an overall accuracy of (95.78% on MRI and 97.44% on CT). Unfortunately, the obtained results were not promising, and there were some limitations due to the different modalities of the dataset. Thereafter, a fusion of MRI and CT-scan datasets generated the fused optimized hybrid-feature dataset. The MLP has shown a promising accuracy of 99% among all the deployed classifiers.
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Singh S, Melnik R. Thermal ablation of biological tissues in disease treatment: A review of computational models and future directions. Electromagn Biol Med 2020; 39:49-88. [PMID: 32233691 DOI: 10.1080/15368378.2020.1741383] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Percutaneous thermal ablation has proven to be an effective modality for treating both benign and malignant tumours in various tissues. Among these modalities, radiofrequency ablation (RFA) is the most promising and widely adopted approach that has been extensively studied in the past decades. Microwave ablation (MWA) is a newly emerging modality that is gaining rapid momentum due to its capability of inducing rapid heating and attaining larger ablation volumes, and its lesser susceptibility to the heat sink effects as compared to RFA. Although the goal of both these therapies is to attain cell death in the target tissue by virtue of heating above 50°C, their underlying mechanism of action and principles greatly differs. Computational modelling is a powerful tool for studying the effect of electromagnetic interactions within the biological tissues and predicting the treatment outcomes during thermal ablative therapies. Such a priori estimation can assist the clinical practitioners during treatment planning with the goal of attaining successful tumour destruction and preservation of the surrounding healthy tissue and critical structures. This review provides current state-of-the-art developments and associated challenges in the computational modelling of thermal ablative techniques, viz., RFA and MWA, as well as touch upon several promising avenues in the modelling of laser ablation, nanoparticles assisted magnetic hyperthermia and non-invasive RFA. The application of RFA in pain relief has been extensively reviewed from modelling point of view. Additionally, future directions have also been provided to improve these models for their successful translation and integration into the hospital work flow.
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Affiliation(s)
- Sundeep Singh
- MS2Discovery Interdisciplinary Research Institute, Wilfrid Laurier University, Waterloo, Ontario, Canada
| | - Roderick Melnik
- MS2Discovery Interdisciplinary Research Institute, Wilfrid Laurier University, Waterloo, Ontario, Canada.,BCAM - Basque Center for Applied Mathematics, Bilbao, Spain
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18
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Integration of a knowledge-based constraint into generative models with applications in semi-automatic segmentation of liver tumors. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101725] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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19
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Chen Y, Wang K, Liao X, Qian Y, Wang Q, Yuan Z, Heng PA. Channel-Unet: A Spatial Channel-Wise Convolutional Neural Network for Liver and Tumors Segmentation. Front Genet 2019; 10:1110. [PMID: 31827487 PMCID: PMC6892404 DOI: 10.3389/fgene.2019.01110] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 10/16/2019] [Indexed: 01/28/2023] Open
Abstract
It is a challenge to automatically and accurately segment the liver and tumors in computed tomography (CT) images, as the problem of over-segmentation or under-segmentation often appears when the Hounsfield unit (Hu) of liver and tumors is close to the Hu of other tissues or background. In this paper, we propose the spatial channel-wise convolution, a convolutional operation along the direction of the channel of feature maps, to extract mapping relationship of spatial information between pixels, which facilitates learning the mapping relationship between pixels in the feature maps and distinguishing the tumors from the liver tissue. In addition, we put forward an iterative extending learning strategy, which optimizes the mapping relationship of spatial information between pixels at different scales and enables spatial channel-wise convolution to map the spatial information between pixels in high-level feature maps. Finally, we propose an end-to-end convolutional neural network called Channel-UNet, which takes UNet as the main structure of the network and adds spatial channel-wise convolution in each up-sampling and down-sampling module. The network can converge the optimized mapping relationship of spatial information between pixels extracted by spatial channel-wise convolution and information extracted by feature maps and realizes multi-scale information fusion. The proposed ChannelUNet is validated by the segmentation task on the 3Dircadb dataset. The Dice values of liver and tumors segmentation were 0.984 and 0.940, which is slightly superior to current best performance. Besides, compared with the current best method, the number of parameters of our method reduces by 25.7%, and the training time of our method reduces by 33.3%. The experimental results demonstrate the efficiency and high accuracy of Channel-UNet in liver and tumors segmentation in CT images.
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Affiliation(s)
- Yilong Chen
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Kai Wang
- AI Research Center, Peng Cheng Laboratory, Shenzhen, China
| | - Xiangyun Liao
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yinling Qian
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen, China
- Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology, Shenzhen Institutes of Advanced Technology, Shenzhen, China
| | - Qiong Wang
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen, China
- Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology, Shenzhen Institutes of Advanced Technology, Shenzhen, China
| | - Zhiyong Yuan
- School of Computer Science, Wuhan University, Wuhan, China
| | - Pheng-Ann Heng
- T Stone Robotics Institute and Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
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20
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Deng Z, Guo Q, Zhu Z. Dynamic Regulation of Level Set Parameters Using 3D Convolutional Neural Network for Liver Tumor Segmentation. JOURNAL OF HEALTHCARE ENGINEERING 2019; 2019:4321645. [PMID: 30918620 PMCID: PMC6409057 DOI: 10.1155/2019/4321645] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 02/06/2019] [Indexed: 02/07/2023]
Abstract
Segmentation of liver tumors plays an important role in the choice of therapeutic strategies for liver disease and treatment monitoring. In this paper, we generalize the process of a level set with a novel algorithm of dynamic regulation to energy functional parameters. The presented method is fully automatic once the tumor has been detected. First, a 3D convolutional neural network with dense layers for classification is used to estimate current contour location relative to the tumor boundary. Second, the output 3D CNN probabilities can dynamically regulate parameters of the level set functional over the process of segmentation. Finally, for full automation, appropriate initializations and local window size are generated based on the current contour position probabilities. We demonstrate the proposed method on the dataset of MICCAI 2017 LiTS Challenge and 3DIRCADb that include low contrast and heterogeneous tumors as well as noisy images. To illustrate the strength of our method, we evaluated it against the state-of-the-art methods. Compared with the level set framework with fixed parameters, our method performed better significantly with an average DICE improvement of 0.15. We also analyzed a challenging dataset 3DIRCADb of tumors and obtained a competitive DICE of 0.85 ± 0.06 with the proposed method.
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Affiliation(s)
- Zhuofu Deng
- College of Software, Northeastern University, Shenyang 110004, China
| | - Qingzhe Guo
- College of Software, Northeastern University, Shenyang 110004, China
| | - Zhiliang Zhu
- College of Software, Northeastern University, Shenyang 110004, China
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21
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Li X, Chen H, Qi X, Dou Q, Fu CW, Heng PA. H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:2663-2674. [PMID: 29994201 DOI: 10.1109/tmi.2018.2845918] [Citation(s) in RCA: 849] [Impact Index Per Article: 121.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Recently, fully convolutional neural networks (FCNs), including 2-D and 3-D FCNs, serve as the backbone in many volumetric image segmentation. However, 2-D convolutions cannot fully leverage the spatial information along the third dimension while 3-D convolutions suffer from high computational cost and GPU memory consumption. To address these issues, we propose a novel hybrid densely connected UNet (H-DenseUNet), which consists of a 2-D DenseUNet for efficiently extracting intra-slice features and a 3-D counterpart for hierarchically aggregating volumetric contexts under the spirit of the auto-context algorithm for liver and tumor segmentation. We formulate the learning process of the H-DenseUNet in an end-to-end manner, where the intra-slice representations and inter-slice features can be jointly optimized through a hybrid feature fusion layer. We extensively evaluated our method on the data set of the MICCAI 2017 Liver Tumor Segmentation Challenge and 3DIRCADb data set. Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.
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22
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Maolood IY, Al-Salhi YEA, Lu S. Thresholding for Medical Image Segmentation for Cancer using Fuzzy Entropy with Level Set Algorithm. Open Med (Wars) 2018; 13:374-383. [PMID: 30211320 PMCID: PMC6132127 DOI: 10.1515/med-2018-0056] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 08/07/2018] [Indexed: 11/15/2022] Open
Abstract
In this study, an effective means for detecting cancer region through different types of medical image segmentation are presented and explained. We proposed a new method for cancer segmentation on the basis of fuzzy entropy with a level set (FELs) thresholding. The proposed method was successfully utilized to segment cancer images and then efficiently performed the segmentation of test ultrasound image, brain MRI, and dermoscopy image compared with algorithms proposed in previous studies. Results showed an excellent performance of the proposed method in detecting cancer image segmentation in terms of accuracy, precision, specificity, and sensitivity measures.
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Affiliation(s)
- Ismail Yaqub Maolood
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | | | - Songfeng Lu
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.,Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen, 518063, China
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23
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A Unified Level Set Framework Combining Hybrid Algorithms for Liver and Liver Tumor Segmentation in CT Images. BIOMED RESEARCH INTERNATIONAL 2018; 2018:3815346. [PMID: 30159326 PMCID: PMC6106976 DOI: 10.1155/2018/3815346] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2018] [Revised: 07/17/2018] [Accepted: 07/26/2018] [Indexed: 12/03/2022]
Abstract
Accurate and reliable segmentation of liver tissue and liver tumor is essential for the follow-up of hepatic diagnosis. In this paper, we present a method for liver segmentation and a method for liver tumor segmentation. The two methods are grounded on a novel unified level set method (LSM), which incorporates both region information and edge information to evolve the contour. This level set framework is more resistant to edge leakage than the single-information driven LSMs for liver segmentation and surpasses many other models for liver tumor segmentation. Specifically, for liver segmentation, a hybrid image preprocessing scheme is used first to convert an input CT image into a binary image. Then with manual setting of a few seed points on the obtained binary image, the following region-growing is performed to extract a rough liver region with no leakage. The unified LSM is proposed at last to refine the segmentation result. For liver tumor segmentation, a local intensity clustering based LSM coupled with hidden Markov random field and expectation-maximization (HMRF-EM) algorithm is applied to construct an enhanced edge indicator for the unified LSM. With this development, expected segmentation results can be obtained via the unified LSM, even for complex tumors. The two methods were evaluated with various datasets containing a local hospital dataset, the public datasets SLIVER07, 3Dircadb, and MIDAS via five measures. The proposed liver segmentation method outperformed other previous semiautomatic methods on the SLIVER07 dataset and required less interaction. The proposed liver tumor segmentation method was also competitive with other state-of-the-art methods in both accuracy and efficiency on the 3Dircadb database. Our methods are evaluated to be accurate and efficient, which allows their adoptions in clinical practice.
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