Semantic-aware Random Convolution and Source Matching for Domain Generalization in Medical Image Segmentation

• URL: http://arxiv.org/abs/2512.01510v1
• Date: Mon, 01 Dec 2025 10:35:45 GMT
• Title: Semantic-aware Random Convolution and Source Matching for Domain Generalization in Medical Image Segmentation
• Authors: Franz Thaler, Martin Urschler, Mateusz Kozinski, Matthias AF Gsell, Gernot Plank, Darko Stern,
• Abstract summary: We tackle the challenging problem of single-source domain generalization (DG) for medical image segmentation.<n>We propose a novel method for promoting DG when training deep segmentation networks, which we call SRCSM.
• Score: 4.617834334211392
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We tackle the challenging problem of single-source domain generalization (DG) for medical image segmentation. To this end, we aim for training a network on one domain (e.g., CT) and directly apply it to a different domain (e.g., MR) without adapting the model and without requiring images or annotations from the new domain during training. We propose a novel method for promoting DG when training deep segmentation networks, which we call SRCSM. During training, our method diversifies the source domain through semantic-aware random convolution, where different regions of a source image are augmented differently, based on their annotation labels. At test-time, we complement the randomization of the training domain via mapping the intensity of target domain images, making them similar to source domain data. We perform a comprehensive evaluation on a variety of cross-modality and cross-center generalization settings for abdominal, whole-heart and prostate segmentation, where we outperform previous DG techniques in a vast majority of experiments. Additionally, we also investigate our method when training on whole-heart CT or MR data and testing on the diastolic and systolic phase of cine MR data captured with different scanner hardware, where we make a step towards closing the domain gap in this even more challenging setting. Overall, our evaluation shows that SRCSM can be considered a new state-of-the-art in DG for medical image segmentation and, moreover, even achieves a segmentation performance that matches the performance of the in-domain baseline in several settings.

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