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.