FLAIRBrainSeg: Fine-Grained Brain Segmentation Using FLAIR MRI Only

Le Bot Edern, Rémi Giraud, Boris Mansencal, Thomas Tourdias, Jose V Manjon, Pierrick Coupe
Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, PMLR 301:393-406, 2026.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v301-edern26a, title = {FLAIRBrainSeg: Fine-Grained Brain Segmentation Using FLAIR MRI Only}, author = {Edern, Le Bot and Giraud, R\'emi and Mansencal, Boris and Tourdias, Thomas and Manjon, Jose V and Coupe, Pierrick}, booktitle = {Proceedings of The 8th International Conference on Medical Imaging with Deep Learning}, pages = {393--406}, year = {2026}, editor = {Tasdizen, Tolga and Elhabian, Shireen and Summers, Ronald and Chen, Chen and Koch, Lisa and Zhuang, Yan}, volume = {301}, series = {Proceedings of Machine Learning Research}, month = {09--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v301/main/assets/edern26a/edern26a.pdf}, url = {https://proceedings.mlr.press/v301/edern26a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T FLAIRBrainSeg: Fine-Grained Brain Segmentation Using FLAIR MRI Only %A Le Bot Edern %A Rémi Giraud %A Boris Mansencal %A Thomas Tourdias %A Jose V Manjon %A Pierrick Coupe %B Proceedings of The 8th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2026 %E Tolga Tasdizen %E Shireen Elhabian %E Ronald Summers %E Chen Chen %E Lisa Koch %E Yan Zhuang %F pmlr-v301-edern26a %I PMLR %P 393--406 %U https://proceedings.mlr.press/v301/edern26a.html %V 301 %X 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.
APA
Edern, L.B., Giraud, R., Mansencal, B., Tourdias, T., Manjon, J.V. & Coupe, P.. (2026). FLAIRBrainSeg: Fine-Grained Brain Segmentation Using FLAIR MRI Only. Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 301:393-406 Available from https://proceedings.mlr.press/v301/edern26a.html.

Related Material