Delineation and quantification of normal and abnormal brain tissues on Magnetic Resonance Images is fundamental to the diagnosis and longitudinal assessment of neurological diseases. Here we sought to develop a convolutional neural network for automated multiclass tissue segmentation of brain MRIs that was robust at typical clinical resolutions and in the presence of a variety of lesions. We trained a 3D U-Net for full brain multiclass tissue segmentation from a prior atlas-based segmentation method on an internal dataset that consisted of 558 clinical T1-weighted brain MRIs (453/52/53; training/validation/test) of patients with one of 50 different diagnostic entities (n = 362) or with a normal brain MRI (n = 196). We then used transfer learning to refine our model on an external dataset that consisted of 7 patients with hand-labeled tissue types. We evaluated the tissue-wise and intra-lesion performance with different loss functions and spatial prior information in the validation set and applied the best performing model to the internal and external test sets. The network achieved an average overall Dice score of 0.87 and volume similarity of 0.97 in the internal test set. Further, the network achieved a median intra-lesion tissue segmentation accuracy of 0.85 inside lesions within white matter and 0.61 inside lesions within gray matter. After transfer learning, the network achieved an average overall Dice score of 0.77 and volume similarity of 0.96 in the external dataset compared to human raters. The network had equivalent or better performance than the original atlas-based method on which it was trained across all metrics and produced segmentations in a hundredth of the time. We anticipate that this pipeline will be a useful tool for clinical decision support and quantitative analysis of clinical brain MRIs in the presence of lesions.

在磁共振图像上描绘和量化正常和异常脑组织是神经系统疾病诊断和纵向评估的基础。在这里，我们试图开发一个卷积神经网络，用于脑 MRI 的自动多类组织分割，该网络在典型的临床分辨率和存在各种病变的情况下是稳健的。我们在内部数据集上训练了一个 3D U-Net，用于全脑多类组织分割，该方法来自先前基于图谱的分割方法，该内部数据集由患者的 558 个临床 T1 加权脑 MRI（453/52/53；训练/验证/测试）组成具有 50 个不同诊断实体之一 (n = 362) 或具有正常脑部 MRI (n = 196)。然后，我们使用迁移学习在外部数据集上改进我们的模型，该数据集由 7 名具有手工标记的组织类型的患者组成。我们在验证集中评估了具有不同损失函数和空间先验信息的组织和病灶内性能，并将最佳性能模型应用于内部和外部测试集。该网络在内部测试集中实现了 0.87 的平均总体 Dice 分数和 0.97 的体积相似度。此外，该网络实现了白质内病灶内 0.85 和灰质内病灶内 0.61 的中位病灶内组织分割精度。在迁移学习之后，与人类评分者相比，网络在外部数据集中实现了 0.77 的平均总体 Dice 得分和 0.96 的体积相似度。该网络的性能与原始的基于地图集的方法相当或更好，该方法在所有指标上进行训练，并在百分之一的时间内产生分割。