Deep Learning in Multi-organ Segmentation

TL;DR

This paper reviews deep learning methods for multi-organ segmentation in medical imaging, categorizing approaches, analyzing their strengths and weaknesses, and comparing their performance on benchmark datasets.

Contribution

It provides a comprehensive classification of DL-based segmentation methods, highlights key advancements, and offers a comparative analysis on standard datasets.

Findings

Deep learning methods have shown significant progress in multi-organ segmentation.

Benchmark datasets enable objective comparison of different approaches.

Identified challenges and future directions for DL in medical segmentation.

Abstract

This paper presents a review of deep learning (DL) in multi-organ segmentation. We summarized the latest DL-based methods for medical image segmentation and applications. These methods were classified into six categories according to their network design. For each category, we listed the surveyed works, highlighted important contributions and identified specific challenges. Following the detailed review of each category, we briefly discussed its achievements, shortcomings and future potentials. We provided a comprehensive comparison among DL-based methods for thoracic and head & neck multiorgan segmentation using benchmark datasets, including the 2017 AAPM Thoracic Auto-segmentation Challenge datasets and 2015 MICCAI Head Neck Auto-Segmentation Challenge datasets.