Degradation-Aware Blur-Segmentation of Brain Tumor

2026-05-15

AI Summary

This work addresses the challenge of multimodal 3D MRI brain tumor segmentation, which is highly susceptible to motion-induced blurring and artifacts that degrade boundary delineation and segmentation performance. To tackle this issue, the authors propose DABSeg, a novel network that unifies degradation-aware joint deblurring and segmentation for the first time. The method incorporates a feature-domain motion deblurring module to compensate for blur and rebalance intensity, enhanced by blur-aware cross-modal attention and multi-scale residual aggregation to improve feature robustness under degradation. Additionally, it employs a clear-reference reconstruction loss combined with a small-lesion-weighted Dice loss for joint optimization. Evaluated on the BraTS2020 dataset, DABSeg significantly outperforms state-of-the-art methods under both pristine and degraded conditions, achieving notable gains in segmentation accuracy for small lesions and boundary regions.

Abstract

Multimodal 3D MRI brain tumor segmentation is a pivotal step in radiotherapy target delineation, surgical planning and post-treatment assessment. Existing methods often assume artifact-free MRI images. However, inevitable patient motion during scanning introduces artifacts and blur that degrade boundary and texture features, leading to poor segmentation performance. To bridge this gap, we introduce Degradation-Aware Blur-Segmentation Net (DABSeg), a synchronous deblurring 3D multimodal MRI segmentation network that unifies blur removal and accurate segmentation. Specifically, we propose a feature-domain motion-deblurring stem to compensate for blur and rebalance intensity. Concurrently, the backbone network embeds a blur-aware cross-modal cross-attention module and multi-scale residual aggregation to yield effective modality complementarity. Notably, we optimize a joint loss that combines weighted Dice with a clear-reference reconstruction term, where imbalanced weights are applied to small targets to boost learning intensity and predictive stability for small lesions and border regions. Systematic comparisons and ablation experiments on the BraTS2020 dataset under both clear and degenerative conditions consistently demonstrate that DABSeg surpasses state-of-the-art methods in tumor Dice score and boundary precision. These results validate the effectiveness of degenerative-aware cross-task collaborative learning in improving the robustness and clinical utility of multi-modal 3D brain tumor segmentation under realistic degenerative conditions. The source code is available at https://github.com/YuchunWang24/DABSeg_ICPR

Problem

• brain tumor segmentation
• motion artifacts
• image blur
• multimodal MRI
• degradation

Innovation

• degradation-aware
• motion deblurring
• cross-modal attention
• joint loss optimization
• 3D brain tumor segmentation