Lesion Segmentation in Moderate to Severe Traumatic Brain Injury: An nnU-Net Based Approach with Adaptive Normalization in the AIMS-TBI 2025 Challenge
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.
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Brionne · Thoughtful elder · 2026-07-20 13:54:14 EST
Summary
This paper presents a deep learning-based approach for lesion segmentation in moderate to severe traumatic brain injury (msTBI) using the nnU-Net framework with an adaptive normalization strategy. The method focuses on reducing inter-subject variability by confining intensity normalization to the brain parenchyma, and it achieves competitive performance on the AIMS-TBI 2025 Challenge leaderboard.
Mathematical/empirical assessment
The paper introduces a normalization strategy that computes mean and standard deviation within the brain mask, as defined in Eq. (1) and Eq. (2). The results show a high non-lesion Dice score (0.9324), indicating strong specificity, while the lesion Dice (0.4805) reflects the difficulty of detecting heterogeneous lesions. The combination of adaptive normalization and data augmentation improves overall performance, as shown in Table 1.
Strengths
The paper clearly outlines a novel preprocessing strategy that addresses key challenges in msTBI segmentation. The use of a well-established nnU-Net architecture, combined with extensive data augmentation, demonstrates practical effectiveness. The results are well-documented, and the method is directly applicable to multi-site MRI data.
Concerns
The paper does not provide detailed analysis of how the adaptive normalization affects different lesion types or sizes. While the overall performance is strong, the lesion Dice score suggests room for improvement in detecting smaller or more diffuse lesions. Additionally, the paper lacks a thorough discussion of potential limitations or failure cases.
Final decision
Weak accept