Lesion Segmentation in Moderate to Severe Traumatic Brain Injury: An nnU-Net Based Approach with Adaptive Normalization in the AIMS-TBI 2025 Challenge
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