Task-oriented Uncertainty Collaborative Learning for Label-Efficient Brain Tumor Segmentation

• URL: http://arxiv.org/abs/2503.05682v1
• Date: Fri, 07 Mar 2025 18:44:53 GMT
• Title: Task-oriented Uncertainty Collaborative Learning for Label-Efficient Brain Tumor Segmentation
• Authors: Zhenxuan Zhang, Hongjie Wu, Jiahao Huang, Baihong Xie, Zhifan Gao, Junxian Du, Pete Lally, Guang Yang,
• Abstract summary: Multi-contrast magnetic resonance imaging (MRI) plays a vital role in brain tumor segmentation and diagnosis.<n>Existing methods still face the challenges of multi-level specificity perception across different contrasts.<n>We propose a Task-oriented Uncertainty Collaborative Learning framework for multi-contrast MRI segmentation.
• Score: 6.722672686635773
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-contrast magnetic resonance imaging (MRI) plays a vital role in brain tumor segmentation and diagnosis by leveraging complementary information from different contrasts. Each contrast highlights specific tumor characteristics, enabling a comprehensive understanding of tumor morphology, edema, and pathological heterogeneity. However, existing methods still face the challenges of multi-level specificity perception across different contrasts, especially with limited annotations. These challenges include data heterogeneity, granularity differences, and interference from redundant information. To address these limitations, we propose a Task-oriented Uncertainty Collaborative Learning (TUCL) framework for multi-contrast MRI segmentation. TUCL introduces a task-oriented prompt attention (TPA) module with intra-prompt and cross-prompt attention mechanisms to dynamically model feature interactions across contrasts and tasks. Additionally, a cyclic process is designed to map the predictions back to the prompt to ensure that the prompts are effectively utilized. In the decoding stage, the TUCL framework proposes a dual-path uncertainty refinement (DUR) strategy which ensures robust segmentation by refining predictions iteratively. Extensive experimental results on limited labeled data demonstrate that TUCL significantly improves segmentation accuracy (88.2\% in Dice and 10.853 mm in HD95). It shows that TUCL has the potential to extract multi-contrast information and reduce the reliance on extensive annotations. The code is available at: https://github.com/Zhenxuan-Zhang/TUCL_BrainSeg.

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