Zero-shot Multi-Contrast Brain MRI Registration by Intensity Randomizing T1-weighted MRI (LUMIR25)

• URL: http://arxiv.org/abs/2602.06292v1
• Date: Fri, 06 Feb 2026 01:17:49 GMT
• Title: Zero-shot Multi-Contrast Brain MRI Registration by Intensity Randomizing T1-weighted MRI (LUMIR25)
• Authors: Hengjie Liu, Yimeng Dou, Di Xu, Xinyi Fu, Dan Ruan, Ke Sheng,
• Abstract summary: We submit to the LUMIR25 challenge in Learn2Reg 2025, which achieved 1st place overall on the test set.<n>This year's task focuses on zero-shot registration under domain shifts (high-field MRI, pathological brains, and various MRI contrasts)<n>We employ three simple but effective strategies to achieve good generalization with diverse contrasts from a model trained with T1-weighted MRI only.
• Score: 9.111478367494433
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we summarize the methods and results of our submission to the LUMIR25 challenge in Learn2Reg 2025, which achieved 1st place overall on the test set. Extended from LUMIR24, this year's task focuses on zero-shot registration under domain shifts (high-field MRI, pathological brains, and various MRI contrasts), while the training data comprise only in-domain T1-weighted brain MRI. We start with a meticulous analysis of LUMIR24 winners to identify the main contributors to good monomodal registration performance. To achieve good generalization with diverse contrasts from a model trained with T1-weighted MRI only, we employ three simple but effective strategies: (i) a multimodal loss based on the modality-independent neighborhood descriptor (MIND), (ii) intensity randomization for appearance augmentation, and (iii) lightweight instance-specific optimization (ISO) on feature encoders at inference time. On the validation set, our approach achieves reasonable T1-T2 registration accuracy while maintaining good deformation regularity.

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