A review of deep learning based methods for medical image multi-organ segmentation. (May 2021)

• Record Type: Journal Article
• Authors: Fu, Yabo; Lei, Yang; Wang, Tonghe; Curran, Walter J.; Liu, Tian; Yang, Xiaofeng
• Is Part Of: Physica medica. Volume 85(2021)
• Page Start: 107
• Page End: 122
• Publication Date: 2021-05
• Subjects: Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2021.05.003
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Comprehensive review of deep learning-based multi-organ segmentation. Categorization of pixel-wise classification and end-to-end segmentation. Pixel-wise classification includes AE and CNN. End-to-end segmentation includes FCN, R-FCN, GAN and synthetic image-aided. Benchmark of algorithms' performances for thoracic and head-neck CT segmentation. Abstract: Deep learning has revolutionized image processing and achieved the-state-of-art performance in many medical image segmentation tasks. Many deep learning-based methods have been published to segment different parts of the body for different medical applications. It is necessary to summarize the current state of development for deep learning in the field of medical image segmentation. In this paper, we aim to provide a comprehensive review with a focus on multi-organ image segmentation, which is crucial for radiotherapy where the tumor and organs-at-risk need to be contoured for treatment planning. We grouped the surveyed methods into two broad categories which are 'pixel-wise classification' and 'end-to-end segmentation'. Each category was divided into subgroups according to their network design. For each type, we listed the surveyed works, highlighted important contributions and identified specific challenges. Following the detailed review, we discussed the achievements, shortcomings and future potentials of each category. To enable direct comparison, we listed the performance of the surveyed works that used thoracic and head-and-neck benchmark datasets.