Automated Claustrum Segmentation in Human Brain MRI Using Deep Learning

• URL: http://arxiv.org/abs/2008.03465v3
• Date: Tue, 23 May 2023 12:11:00 GMT
• Title: Automated Claustrum Segmentation in Human Brain MRI Using Deep Learning
• Authors: Hongwei Li, Aurore Menegaux, Benita Schmitz-Koep, Antonia Neubauer, Felix JB B\"auerlein, Suprosanna Shit, Christian Sorg, Bjoern Menze and Dennis Hedderich
• Abstract summary: We present a multi-view Deep Learning-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 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.
• Score: 3.263309103832064
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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