Laso Pablo, Cerri Stefano, Sorby-Adams Annabel, Guo Jennifer, Mateen Farrah, Goebl Philipp, Wu Jiaming, Liu Peirong, Li Hongwei, Young Sean I, Billot Benjamin, Puonti Oula, Sze Gordon, Payabavash Sam, DeHavenon Adam, Sheth Kevin N, Rosen Matthew S, Kirsch John, Strisciuglio Nicola, Wolterink Jelmer M, Eshaghi Arman, Barkhof Frederik, Kimberly W Taylor, Iglesias Juan Eugenio
Massachusetts General Hospital, Harvard Medical School, USA.
Electrical Engineering, Mathematics and Computer Science, University of Twente, The Netherlands.
Proc IEEE Int Symp Biomed Imaging. 2024 May;2024. doi: 10.1109/isbi56570.2024.10635502. Epub 2024 Aug 22.
Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis. Automated segmentation and quantification is desirable but existing methods require high-resolution MRI with good signal-to-noise ratio (SNR). This precludes application to clinical and low-field portable MRI (pMRI) scans, thus hampering large-scale tracking of atrophy and WMH progression, especially in underserved areas where pMRI has huge potential. Here we present a method that segments white matter hyperintensity and 36 brain regions from scans of any resolution and contrast (including pMRI) without retraining. We show results on eight public datasets and on a private dataset with paired high- and low-field scans (3T and 64mT), where we attain strong correlation between the WMH (=.85) and hippocampal volumes (=.89) estimated at both fields. Our method is publicly available as part of FreeSurfer, at: http://surfer.nmr.mgh.harvard.edu/fswiki/WMH-SynthSeg.
脑萎缩和白质高信号（WMH）是确定脑血管疾病和多发性硬化症脑损伤的关键神经影像学特征。自动分割和量化是理想的，但现有方法需要具有良好信噪比（SNR）的高分辨率MRI。这排除了其应用于临床和低场便携式MRI（pMRI）扫描的可能性，从而阻碍了对萎缩和WMH进展的大规模跟踪，尤其是在pMRI具有巨大潜力的服务不足地区。在此，我们提出了一种方法，该方法可以从任何分辨率和对比度（包括pMRI）的扫描中分割白质高信号和36个脑区，而无需重新训练。我们在八个公共数据集和一个包含配对高场和低场扫描（3T和64mT）的私人数据集上展示了结果，在这些数据集中，我们在两个场强下估计的WMH（=0.85）和海马体积（=0.89）之间获得了很强的相关性。我们的方法作为FreeSurfer的一部分公开可用，网址为：http://surfer.nmr.mgh.harvard.edu/fswiki/WMH-SynthSeg 。
