An Overview of Abdominal Multi-Organ Segmentation

(E-pub Ahead of Print)

Author(s): Qiang Li, Hong Song*, Lei Chen, Xianqi Meng, Jian Yang, Le Zhang.

Journal Name: Current Bioinformatics

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Abstract:

The segmentation of multiple abdominal organs of the human body from images with different modalities is challenging because of the inter-subject variance among abdomens, as well as the complex intra-subject variance among organs. In this paper, the recent methods proposed for abdominal multi-organ segmentation (AMOS) on medical images in the literature are reviewed. The AMOS methods can be categorized into traditional and deep learning-based methods. First, various approaches, techniques, recent advances, and related problems under both segmentation categories are explained. Second, the advantages and disadvantages of these methods are discussed. A summary of some public datasets for AMOS is provided. Finally, AMOS remains an open issue, and the combination of different methods can achieve improved segmentation performance.

Keywords: Multi-organ segmentation, Deep learning, Datasets for AMOS, Segmentation performance.

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Article Details

(E-pub Ahead of Print)
DOI: 10.2174/1574893615999200425232601
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