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Supervised and Semi-supervised Methods for Abdominal Organ Segmentation: A Review

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  • Pattern Recognition & Computer Vision
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Abstract

Abdominal organ segmentation is the segregation of a single or multiple abdominal organ(s) into semantic image segments of pixels identified with homogeneous features such as color and texture, and intensity. The abdominal organ(s) condition is mostly connected with greater morbidity and mortality. Most patients often have asymptomatic abdominal conditions and symptoms, which are often recognized late; hence the abdomen has been the third most common cause of damage to the human body. That notwithstanding, there may be improved outcomes where the condition of an abdominal organ is detected earlier. Over the years, supervised and semi-supervised machine learning methods have been used to segment abdominal organ(s) in order to detect the organ(s) condition. The supervised methods perform well when the used training data represents the target data, but the methods require large manually annotated data and have adaptation problems. The semi-supervised methods are fast but record poor performance than the supervised if assumptions about the data fail to hold. Current state-of-the-art methods of supervised segmentation are largely based on deep learning techniques due to their good accuracy and success in real world applications. Though it requires a large amount of training data for automatic feature extraction, deep learning can hardly be used. As regards the semi-supervised methods of segmentation, self-training and graph-based techniques have attracted much research attention. Self-training can be used with any classifier but does not have a mechanism to rectify mistakes early. Graph-based techniques thrive on their convexity, scalability, and effectiveness in application but have an out-of-sample problem. In this review paper, a study has been carried out on supervised and semi-supervised methods of performing abdominal organ segmentation. An observation of the current approaches, connection and gaps are identified, and prospective future research opportunities are enumerated.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Nos. 61772242, 61976106 and 61572239), the China Postdoctoral Science Foundation (No. 2017M611737), the Six Talent Peaks Project in Jiangsu Province (No. DZXX-122), and the Key Special Project of Health and Family Planning Science and Technology in Zhenjiang City (No. SHW2017019). The authors would like to thank the Radiologists of the Medical Imaging Department of Affiliated Hospital of Jiangsu University.

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  1. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, 212013, China

    Isaac Baffour Senkyire & Zhe Liu

  2. Computer Science Department, Ghana Communication Technology University, Accra, Ghana

    Isaac Baffour Senkyire

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  1. Isaac Baffour Senkyire
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Correspondence to Zhe Liu.

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Colored figures are available in the online version at https://link.springer.com/journal/11633

Isaac Baffour Senkyire received the B. Sc. degree in computer science from Department of Computer Science, Kwame Nkrumah University of Science and Technology (KNUST), Ghana in 2009, and the M. Sc. degree in information security and audit from Department of Computing and Information Systems, University of Greenwich, UK in 2014. He is a lecturer at Computer Science Department of Ghana Communication Technology University, Ghana. He is currently a Ph. D. degree candidate with School of Computer Science and Communication Engineering, Jiangsu University, China.

His research interests include medical image processing and pattern recognition.

Zhe Liu received the Ph. D. degree in computer science from Jiangsu University, China in 2012. She is a visiting scholar of Department of Radiology, University of Pittsburgh Medical Center, USA, and also a professor at School of Computer Science and Communication Engineering, Jiangsu University, China. She is a member of CCF.

Her research interests include image processing, data mining and pattern recognition.

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Senkyire, I.B., Liu, Z. Supervised and Semi-supervised Methods for Abdominal Organ Segmentation: A Review. Int. J. Autom. Comput. 18, 887–914 (2021). https://doi.org/10.1007/s11633-021-1313-0

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