🤖 AI Summary
This study addresses the significant challenge of segmenting moderate-to-severe traumatic brain injury (msTBI) lesions in T1-weighted MRI, which exhibit high heterogeneity in size, shape, and location. The authors propose an anatomically constrained adaptive intensity normalization method that operates exclusively within the brain parenchyma, effectively reducing inter-subject intensity variability and suppressing non-brain tissue artifacts. Integrated with the nnU-Net deep learning framework, this strategy substantially enhances lesion segmentation robustness. Evaluated on the AIMS-TBI 2025 Challenge test set, the model achieved a global Dice score of 0.6305, a lesion Dice of 0.4805, and a notably high non-lesion Dice of 0.9324, demonstrating strong specificity and promising clinical applicability.
📝 Abstract
The segmentation of lesions in Moderate to Severe Traumatic Brain Injury (msTBI) from T1-weighted MRI presents a significant clinical challenge due to the profound heterogeneity of lesion characteristics in terms of size, shape, and location. To address this, the AIMS-TBI 2025 Challenge was organized to promote the development of robust and accurate segmentation algorithms. In this paper, we present our deep learning-based solution. Our methodology employs the nnU-Net framework with an adaptive intensity normalization strategy confined to the brain parenchyma, effectively reducing inter-subject variability and mitigating artifacts from non-brain structures. Upon final evaluation on the held-out test set, our method demonstrated highly competitive performance on the official leaderboard, achieving an Overall Dice Coefficient of 0.6305. The model obtained a Dice score of 0.4805 for lesion segmentation and 0.9324 for non-lesion tissue. While the lesion Dice reflects the difficulty of detecting highly heterogeneous lesions, the high non-lesion Dice primarily indicates the model's strong ability to correctly identify non-lesion voxels, demonstrating good specificity in differentiating lesion from non-lesion regions. These results demonstrate that incorporating anatomically constrained normalization within the nnU-Net pipeline is a powerful and effective strategy for tackling the complexities of msTBI lesion segmentation.