论文标题
多发性硬化症中同时全脑和病变分割的对比度适应方法
A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
论文作者
论文摘要
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扫描中同时分割白质病变和正常的神经解剖结构的方法。该方法将白质病变的新型模型集成到了先前验证的生成模型中，用于全脑分割。通过对解剖结构的形状进行单独的模型及其在MRI中的外观，该算法可以适应具有不同的扫描仪和成像协议所获得的数据而无需重新培训。我们使用四个不同数据集验证该方法，在白质病变分段中显示出稳健的性能，同时分割了数十个其他大脑结构。我们进一步证明，对比自适应方法也可以安全地应用于健康对照的MRI扫描，并复制MS中深灰质结构中先前记录的先前记录的萎缩模式。该算法是开源神经影像套装Freesurfer的一部分公开使用的。
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.
