FLAIRBrainSeg: Fine-Grained Brain Segmentation Using FLAIR MRI Only

Le Bot Edern, Rémi Giraud, Boris Mansencal, Thomas tourdias, Jose V Manjon, Pierrick Coupe

Keywords: Fine-grained segmentation; Magnetic Resonance Imaging; FLAIR; Convolutional Neural Networks

TL;DR: A novel method for FLAIR-based fine-grained segmentation.

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.

Primary Subject Area: Segmentation

Secondary Subject Area: Transfer Learning and Domain Adaptation

Paper Type: Validation or Application

Registration Requirement: Yes

Submission Number: 138