Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation

• URL: http://arxiv.org/abs/2206.13737v2
• Date: Wed, 29 Jun 2022 20:23:27 GMT
• Title: Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation
• Authors: Yanwu Xu, Shaoan Xie, Maxwell Reynolds, Matthew Ragoza, Mingming Gong, and Kayhan Batmanghelich
• Abstract summary: Domain Generalization (DG) methods for organ segmentation require training data from multiple domains during training. We propose a novel adversarial domain generalization method for organ segmentation trained on data from a emphsingle domain. We evaluate our method for various organ segmentation for unseen modalities, scanning protocols, and scanner sites.
• Score: 35.84892917309007
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: An organ segmentation method that can generalize to unseen contrasts and scanner settings can significantly reduce the need for retraining of deep learning models. Domain Generalization (DG) aims to achieve this goal. However, most DG methods for segmentation require training data from multiple domains during training. We propose a novel adversarial domain generalization method for organ segmentation trained on data from a \emph{single} domain. We synthesize the new domains via learning an adversarial domain synthesizer (ADS) and presume that the synthetic domains cover a large enough area of plausible distributions so that unseen domains can be interpolated from synthetic domains. We propose a mutual information regularizer to enforce the semantic consistency between images from the synthetic domains, which can be estimated by patch-level contrastive learning. We evaluate our method for various organ segmentation for unseen modalities, scanning protocols, and scanner sites.

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