概化理论
分割
计算机科学
人工智能
编码(集合论)
图像(数学)
领域(数学分析)
代表(政治)
一般化
模式识别(心理学)
图像分割
计算机视觉
数学
数学分析
统计
集合(抽象数据类型)
政治
政治学
法学
程序设计语言
作者
Ran Gu,Guotai Wang,Jiangshan Lu,Jingyang Zhang,Wenhui Lei,Yinan Chen,Wenjun Liao,Shichuan Zhang,Kang Li,Dimitris N. Metaxas,Shaoting Zhang
标识
DOI:10.1016/j.media.2023.102904
摘要
Generalization to previously unseen images with potential domain shifts is essential for clinically applicable medical image segmentation. Disentangling domain-specific and domain-invariant features is key for Domain Generalization (DG). However, existing DG methods struggle to achieve effective disentanglement. To address this problem, we propose an efficient framework called Contrastive Domain Disentanglement and Style Augmentation (CDDSA) for generalizable medical image segmentation. First, a disentangle network decomposes the image into domain-invariant anatomical representation and domain-specific style code, where the former is sent for further segmentation that is not affected by domain shift, and the disentanglement is regularized by a decoder that combines the anatomical representation and style code to reconstruct the original image. Second, to achieve better disentanglement, a contrastive loss is proposed to encourage the style codes from the same domain and different domains to be compact and divergent, respectively. Finally, to further improve generalizability, we propose a style augmentation strategy to synthesize images with various unseen styles in real time while maintaining anatomical information. Comprehensive experiments on a public multi-site fundus image dataset and an in-house multi-site Nasopharyngeal Carcinoma Magnetic Resonance Image (NPC-MRI) dataset show that the proposed CDDSA achieved remarkable generalizability across different domains, and it outperformed several state-of-the-art methods in generalizable segmentation. Code is available at https://github.com/HiLab-git/DAG4MIA.
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