Department of Health Technology, Technical University of Denmark, Denmark; Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital Hvidovre, Denmark.
Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital Hvidovre, Denmark.
Neuroimage. 2021 Jan 15;225:117471. doi: 10.1016/j.neuroimage.2020.117471. Epub 2020 Oct 22.
Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients. The method integrates a novel model for white matter lesions into a previously validated generative model for whole-brain segmentation. By using separate models for the shape of anatomical structures and their appearance in MRI, the algorithm can adapt to data acquired with different scanners and imaging protocols without retraining. We validate the method using four disparate datasets, showing robust performance in white matter lesion segmentation while simultaneously segmenting dozens of other brain structures. We further demonstrate that the contrast-adaptive method can also be safely applied to MRI scans of healthy controls, and replicate previously documented atrophy patterns in deep gray matter structures in MS. The algorithm is publicly available as part of the open-source neuroimaging package FreeSurfer.
在这里，我们提出了一种从多发性硬化症患者的多对比度脑 MRI 扫描中同时分割脑白质病变和正常神经解剖结构的方法。该方法将一种新的脑白质病变模型集成到以前验证的全脑分割生成模型中。通过为解剖结构的形状及其在 MRI 中的表现使用单独的模型，该算法可以适应不同扫描仪和成像协议采集的数据，而无需重新训练。我们使用四个不同的数据集验证了该方法，该方法在脑白质病变分割方面表现出强大的性能，同时还可以分割数十个其他脑结构。我们进一步证明，这种对比自适应方法也可以安全地应用于健康对照者的 MRI 扫描，并复制 MS 中深部灰质结构的先前记录的萎缩模式。该算法作为开源神经影像学软件包 FreeSurfer 的一部分提供。
