Supervised and Semi-supervised Methods for Abdominalm Organ Segmentation: A Review
文献类型：期刊论文
| 作者 | Isaac Baffour Senkyire1,2 |
| 刊名 | International Journal of Automation and Computing
 |
| 出版日期 | 2021 |
| 卷号 | 18期号:6页码:887-914 |
| 关键词 | Abdominal organ, supervised segmentation semi-supervised segmentation evaluation metrics image segmentation machine learning |
| ISSN号 | 1476-8186 |
| DOI | 10.1007/s11633-021-1313-0 |
| 英文摘要 | 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/46097]  |
| 专题 | 自动化研究所_学术期刊_International Journal of Automation and Computing |
| 作者单位 | 1.School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China 2.Computer Science Department, Ghana Communication Technology University, Accra, Ghana |
| 推荐引用方式 GB/T 7714 | Isaac Baffour Senkyire. Supervised and Semi-supervised Methods for Abdominalm Organ Segmentation: A Review[J]. International Journal of Automation and Computing,2021,18(6):887-914. |
| APA | Isaac Baffour Senkyire.(2021).Supervised and Semi-supervised Methods for Abdominalm Organ Segmentation: A Review.International Journal of Automation and Computing,18(6),887-914. |
| MLA | Isaac Baffour Senkyire."Supervised and Semi-supervised Methods for Abdominalm Organ Segmentation: A Review".International Journal of Automation and Computing 18.6(2021):887-914. |
入库方式： OAI收割
来源：自动化研究所
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