This paper introduces a novel method for brain segmentation using only FLAIR MRIs, specifically targeting cases where access to other imaging modalities is limited. By leveraging existing automatic segmentation methods, we train a network to approximate segmentations, typically obtained from T1-weighted MRIs. Our method, called FLAIRBrainSeg, produces segmentations of 132 structures and is robust to multiple sclerosis lesions. Experiments on both in-domain and out-of-domain datasets demonstrate that our method outperforms modality-agnostic approaches based on image synthesis, the only currently available alternative for performing brain parcellation using FLAIR MRI alone. This technique holds promise for scenarios where T1-weighted MRIs are unavailable and offers a valuable alternative for clinicians and researchers in need of reliable anatomical segmentation.
View on arXiv@article{bot2025_2504.03376,
title={ FLAIRBrainSeg: Fine-grained brain segmentation using FLAIR MRI only },
author={ Edern Le Bot and Rémi Giraud and Boris Mansencal and Thomas Tourdias and Josè V. Manjon and Pierrick Coupé },
journal={arXiv preprint arXiv:2504.03376},
year={ 2025 }
}