Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation

• URL: http://arxiv.org/abs/2406.02125v1
• Date: Tue, 4 Jun 2024 09:10:02 GMT
• Title: Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation
• Authors: Hao Chen, Hongrun Zhang, U Wang Chan, Rui Yin, Xiaofei Wang, Chao Li,
• Abstract summary: We propose a new framework, named textitDomain Game, to perform better feature distangling for medical image segmentation. In domain game, a set of randomly transformed images derived from a singular source image is strategically encoded into two separate feature sets. Results from cross-site test domain evaluation showcase approximately an 11.8% performance boost in prostate segmentation and around 10.5% in brain tumor segmentation.
• Score: 9.453879758234379
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Single domain generalization aims to address the challenge of out-of-distribution generalization problem with only one source domain available. Feature distanglement is a classic solution to this purpose, where the extracted task-related feature is presumed to be resilient to domain shift. However, the absence of references from other domains in a single-domain scenario poses significant uncertainty in feature disentanglement (ill-posedness). In this paper, we propose a new framework, named \textit{Domain Game}, to perform better feature distangling for medical image segmentation, based on the observation that diagnostic relevant features are more sensitive to geometric transformations, whilist domain-specific features probably will remain invariant to such operations. In domain game, a set of randomly transformed images derived from a singular source image is strategically encoded into two separate feature sets to represent diagnostic features and domain-specific features, respectively, and we apply forces to pull or repel them in the feature space, accordingly. Results from cross-site test domain evaluation showcase approximately an ~11.8% performance boost in prostate segmentation and around ~10.5% in brain tumor segmentation compared to the second-best method.

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