Accurate detection and segmentation of multiple sclerosis (MS) brain lesions on magnetic resonance images are important for disease diagnosis and treatment. This is a challenging task as lesions vary greatly in size, shape, location, and image contrast. The objective of our study was to develop an algorithm based on deep convolutional neural network integrated with anatomic information and lesion-wise loss function (ALL-Net) for fast and accurate automated segmentation of MS lesions. Distance transformation mapping was used to construct a convolutional module that encoded lesion-specific anatomical information. To overcome the lesion size imbalance during network training and improve the detection of small lesions, a lesion-wise loss function was developed in which individual lesions were modeled as spheres of equal size. On the ISBI-2015 longitudinal MS lesion segmentation challenge dataset (19 subjects in total), ALL-Net achieved an overall score of 93.32 and was amongst the top performing methods. On the larger Cornell MS dataset (176 subjects in total), ALL-Net significantly improved both voxel-wise metrics (Dice improvement of 3.9% to 35.3% with p-values ranging from p < 0.01 to p < 0.0001, and AUC of voxel-wise precision-recall curve improvement of 2.1% to 29.8%) and lesion-wise metrics (lesion-wise F1 score improvement of 12.6% to 29.8% with all p-values p < 0.0001, and AUC of lesion-wise ROC curve improvement of 1.4% to 20.0%) compared to leading publicly available MS lesion segmentation tools.

磁共振图像上多发性硬化症（MS）脑部病变的准确检测和分割对于疾病诊断和治疗具有重要意义。这是一项具有挑战性的任务，因为病变的大小、形状、位置和图像对比度差异很大。我们研究的目的是开发一种基于深度卷积神经网络的算法，该算法与解剖信息和病变损失函数 (ALL-Net) 相结合，用于快速、准确地自动分割 MS 病变。距离变换映射用于构建编码特定病变解剖信息的卷积模块。为了克服网络训练期间病变大小不平衡的问题并提高对小病变的检测，开发了一种病变损失函数，其中单个病变被建模为大小相等的球体。在 ISBI-2015 纵向 MS 病变分割挑战数据集（总共 19 个受试者）上，ALL-Net 的总体得分为 93.32，是表现最好的方法之一。在更大的康奈尔 MS 数据集（总共 176 个受试者）上，ALL-Net 显着改善了两个体素指标（Dice 改善了 3.9% 到 35.3%，p 值范围从 p < 0.01 到 p < 0.0001，AUC体素方面的精确回忆曲线提高了 2.1% 到 29.8%）和病变方面的指标（病变方面的 F1 分数提高了 12.6% 到 29.8%，所有 p 值 p < 0.0001，以及病变方面的 AUC）与领先的公开可用的 MS 病变分割工具相比，ROC 曲线提高了 1.4% 至 20.0%。