仅用FLAIR MRI实现精细脑部结构分割，适用于无T1影像场景
FLAIRBrainSeg: Fine-grained brain segmentation using FLAIR MRI only
- 基于现有分割方法训练网络，从FLAIR图像逼近T1加权图的分割结果
- 可分割132个脑区结构，对多发性硬化病灶具有鲁棒性
- 适合临床和研究中缺乏T1影像时的解剖分割需求
本文提出一种仅使用FLAIR MRI进行脑部分割的新方法，针对无法获取其他影像模态的情况。通过利用已有自动分割方法，训练网络以近似通常由T1加权MRI获得的分割结果。所提方法FLAIRBrainSeg可实现132个脑结构的分割，对多发性硬化病变具有鲁棒性。在域内与域外数据集上的实验表明，该方法优于当前唯一可用的基于图像合成的模态无关方法，在仅使用FLAIR MRI进行脑分区时表现更优。该技术为缺乏T1加权MRI时提供了可靠的解剖分割方案，对临床与研究应用具有重要价值。
原文摘要 · Abstract (English)
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.
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