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Structural alterations of the midsagittal corpus callosum (midCC) have beenassociated with a wide range of brain disorders. The midCC is visible on mostMRI contrasts and in many acquisitions with a limited field-of-view. Here, wepresent an automated tool for segmenting and assessing the shape of the midCCfrom T1w, T2w, and FLAIR images. We train a UNet on images from multiple publicdatasets to obtain midCC segmentations. A quality control algorithm is alsobuilt-in, trained on the midCC shape features. We calculate intraclasscorrelations (ICC) and average Dice scores in a test-retest dataset to assesssegmentation reliability. We test our segmentation on poor quality and partialbrain scans. We highlight the biological significance of our extracted featuresusing data from over 40,000 individuals from the UK Biobank; we classifyclinically defined shape abnormalities and perform genetic analyses.

A Comprehensive Corpus Callosum Segmentation Tool for Detecting Callosal Abnormalities and Genetic Associations from Multi Contrast MRIs