In the last two decades, neuroscience has produced intriguing evidence for a central role of the claustrum in mammalian forebrain structure and function. However, relatively few in vivo studies of the claustrum exist in humans. A reason for this may be the delicate and sheet-like structure of the claustrum lying between the insular cortex and the putamen, which makes it not amenable to conventional segmentation methods. Recently, Deep Learning (DL) based approaches have been successfully introduced for automated segmentation of complex, subcortical brain structures. In the following, we present a multi-view DL-based approach to segment the claustrum in T1-weighted MRI scans. We trained and evaluated the proposed method in 181 individuals, using bilateral manual claustrum annotations by an expert neuroradiologist as the reference standard. Cross-validation experiments yielded median volumetric similarity, robust Hausdorff distance, and Dice score of 93.3%, 1.41mm, and 71.8%, respectively, representing equal or superior segmentation performance compared to human intra-rater reliability. The leave-one-scanner-out evaluation showed good transferability of the algorithm to images from unseen scanners at slightly inferior performance. Furthermore, we found that DL-based claustrum segmentation benefits from multi-view information and requires a sample size of around 75 MRI scans in the training set. We conclude that the developed algorithm allows for robust automated claustrum segmentation and thus yields considerable potential for facilitating MRI-based research of the human claustrum. The software and models of our method are made publicly available.

翻译：在过去二十年间，神经科学已获得令人瞩目的证据，表明屏状核在哺乳动物前脑结构与功能中发挥核心作用。然而，针对人类屏状核的活体研究相对匮乏。其原因可能在于屏状核位于岛叶皮层与壳核之间，具有薄层状精细结构，难以采用传统分割方法处理。近年来，基于深度学习的方法已成功应用于复杂皮层下脑结构的自动分割。本文提出一种基于多视图深度学习的屏状核分割方法，用于处理T1加权MRI扫描数据。我们以181名受试者的双侧屏状核专家神经放射科医师手动标注为金标准，对所述方法进行训练与评估。交叉验证实验结果显示，中位体积相似性、稳健Hausdorff距离及Dice评分分别为93.3%、1.41毫米和71.8%，其分割性能达到或优于人类自身评分者信度。留一扫描仪评估表明，该算法对未见过的扫描仪图像具有良好迁移性，但性能略有下降。此外，我们发现基于深度学习的屏状核分割受益于多视图信息，且训练集样本量需达到约75例MRI扫描。我们得出结论，该算法可实现稳健的屏状核自动分割，从而为促进基于MRI的人类屏状核研究提供重要潜力。本方法涉及的软件与模型已公开。