DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing Modalities

• URL: http://arxiv.org/abs/2505.11921v1
• Date: Sat, 17 May 2025 09:12:08 GMT
• Title: DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing Modalities
• Authors: Haitao Li, Ziyu Li, Yiheng Mao, Zhengyao Ding, Zhengxing Huang,
• Abstract summary: We propose DC-Seg, a new method that explicitly disentangles images into modality-invariant anatomical representation.<n>This solution improves the separation of anatomical and modality-specific features by considering the modality gaps.<n>Experiments on the BraTS 2020 and a private white matter hyperintensity(WMH) segmentation dataset demonstrate that DC-Seg outperforms state-of-the-art methods.
• Score: 9.878680177357454
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accurate segmentation of brain images typically requires the integration of complementary information from multiple image modalities. However, clinical data for all modalities may not be available for every patient, creating a significant challenge. To address this, previous studies encode multiple modalities into a shared latent space. While somewhat effective, it remains suboptimal, as each modality contains distinct and valuable information. In this study, we propose DC-Seg (Disentangled Contrastive Learning for Segmentation), a new method that explicitly disentangles images into modality-invariant anatomical representation and modality-specific representation, by using anatomical contrastive learning and modality contrastive learning respectively. This solution improves the separation of anatomical and modality-specific features by considering the modality gaps, leading to more robust representations. Furthermore, we introduce a segmentation-based regularizer that enhances the model's robustness to missing modalities. Extensive experiments on the BraTS 2020 and a private white matter hyperintensity(WMH) segmentation dataset demonstrate that DC-Seg outperforms state-of-the-art methods in handling incomplete multimodal brain tumor segmentation tasks with varying missing modalities, while also demonstrate strong generalizability in WMH segmentation. The code is available at https://github.com/CuCl-2/DC-Seg.

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