Paper Info
Title
MTL-ABS<sup>3</sup>Net: Atlas-Based Semi-Supervised Organ Segmentation Network With Multi-Task Learning for Medical Images
Abstract
Organ segmentation is one of the most important step for various medical image analysis tasks. Recently, semi-supervised learning (SSL) has attracted much attentions by reducing labeling cost. However, most of the existing SSLs neglected the prior shape and position information specialized in the medical images, leading to unsatisfactory localization and non-smooth of objects. In this paper, we propose a novel atlas-based semi-supervised segmentation network with multi-task learning for medical organs, named MTL-ABS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Net, which incorporates the anatomical priors and makes full use of unlabeled data in a self-training and multi-task learning manner. The MTL-ABS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Net consists of two components: an Atlas-Based Semi-Supervised Segmentation Network (ABS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Net) and Reconstruction-Assisted Module (RAM). Specifically, the ABS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Net improves the existing SSLs by utilizing atlas prior, which generates credible pseudo labels in a self-training manner; while the RAM further assists the segmentation network by capturing the anatomical structures from the original images in a multi-task learning manner. Better reconstruction quality is achieved by using MS-SSIM loss function, which further improves the segmentation accuracy. Experimental results from the liver and spleen datasets demonstrated that the performance of our method was significantly improved compared to existing state-of-the-art methods.
Year
DOI
Venue
2022
10.1109/JBHI.2022.3153406
IEEE Journal of Biomedical and Health Informatics
Keywords
DocType
Volume
Abdomen,Humans,Image Processing, Computer-Assisted,Spleen,Supervised Machine Learning
Journal
26
Issue
ISSN
Citations 
8
2168-2194
0
PageRank 
References 
Authors
0.34
14
12
Name
Order
Citations
PageRank
Huimin Huang100.34
Qingqing Chen263.86
Lanfen Lin37824.70
M. Cai4465.20
Qiaowei Zhang500.34
Yutaro Iwamoto600.68
Xian-Hua Han71410.19
Akira Furukawa800.34
Shuzo Kanasaki900.34
Yen-Wei Chen10720155.73
Ruofeng Tong1146649.69
Hongjie Hu12119.50