The presence of cortical lesions in multiple sclerosis patients has emerged as an important biomarker of the disease. They appear in the earliest stages of the illness and have been shown to correlate with the severity of clinical symptoms. However, cortical lesions are hardly visible in conventional magnetic resonance imaging (MRI) at 3T, and thus their automated detection has been so far little explored. In this study, we propose a fully-convolutional deep learning approach, based on the 3D U-Net, for the automated segmentation of cortical and white matter lesions at 3T. For this purpose, we consider a clinically plausible MRI setting consisting of two MRI contrasts only: one conventional T2-weighted sequence (FLAIR), and one specialized T1-weighted sequence (MP2RAGE). We include 90 patients from two different centers with a total of 728 and 3856 gray and white matter lesions, respectively. We show that two reference methods developed for white matter lesion segmentation are inadequate to detect small cortical lesions, whereas our proposed framework is able to achieve a detection rate of 76% for both cortical and white matter lesions with a false positive rate of 29% in comparison to manual segmentation. Further results suggest that our framework generalizes well for both types of lesion in subjects acquired in two hospitals with different scanners.

多发性硬化症患者中皮质病变的存在已成为该疾病的重要生物标志物。它们出现在疾病的最早阶段，并已显示出与临床症状的严重程度相关。然而，在3T的常规磁共振成像（MRI）中几乎看不到皮质病变，因此，迄今为止，尚未对其自动检测进行探索。在这项研究中，我们提出了一种基于3D U-Net的全卷积深度学习方法，用于在3T时自动分割皮质和白质病变。为此，我们认为临床上可行的MRI设置仅由两个MRI对比组成：一个常规的T2加权序列（FLAIR）和一个特殊的T1加权序列（MP2RAGE）。我们纳入了来自两个不同中心的90名患者，分别有728和3856例灰白色病变。我们表明，为白质病变分割开发的两种参考方法不足以检测小皮层病变，而我们提出的框架能够实现皮层和白质病变的检出率均为76％，而假阳性率为29％。与手动细分相比。进一步的结果表明，对于在两家医院使用不同扫描仪获得的受试者中的两种类型的病变，我们的框架都能很好地概括。相较于人工分割，我们提出的框架能够同时针对皮层和白质病灶实现76％的检出率，假阳性率为29％。进一步的结果表明，对于在两家医院使用不同扫描仪获得的受试者中的两种类型的病变，我们的框架都能很好地概括。相较于人工分割，我们提出的框架能够同时针对皮层和白质病变实现76％的检出率，假阳性率为29％。进一步的结果表明，对于在两家医院使用不同扫描仪获得的受试者中的两种类型的病变，我们的框架都能很好地概括。