  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.
