dominal organ segmentation is an essential and fundamental medical procedure with many clinical and research applications. There is extensive variability in the size

location

and shape of the abdominal organs among individuals

and neighboring organs and structures exhibit similar textures and levels of intensity

which contribute to the difficulties encountered when developing robust

accurate

and automated segmentation approaches. In the past decade

deep learning (DL)-based methods have shown promising results based on a large amount of labeled data. However

acquiring large-scale images with high-quality annotations is both difficult and impractical in clinical practice. Furthermore

the images obtained from multi-centers contain domain shift

which degenerates the model’s performance on any new and unseen dataset. There have been extensive efforts to develop limited-supervised segmentation methods that can tackle this imperfect data problem. At the same time

prior knowledge from the medical domain may improve model performance while also constraining the results to an anatomically plausible range. In this paper

we provide a review of DL-based methods for abdominal organ segmentation that covers supervised and limited-supervised segmentation techniques

as well as the utilization of prior knowledge of abdominal organ and strategies in DL models. We present a categorized methodological overview of these approaches and summarize the relevant benchmarks and evaluation metrics used in this research area. Finally

we discuss the challenges and potential trends that may emerge in abdominal segmentation. Accordingly

this review systematically synthesizes advancements in DL for abdominal organ segmentation

provides relevant references for researchers in this field

and promotes the transformation of DL techniques into more precise clinical tools for this domain.