Segmenting striatal subregions can be difficult; wherein atlas-based approaches have been shown to be less reliable in patient populations and have problems segmenting smaller striatal ROI’s. We developed a Multi-Task Learning model to segment multiple 3D striatal subregions using a Convolutional Neural Network and compared it to the Clinical Imaging Center atlas (CIC). Dice Score Coefficient and multi-modal objective assessment (PET and fMRI) were conducted to evaluate the reliability of MTL-generated segmentations compared to atlas-based. Overall, MTL-generated segmentations were more comparable to manual than CIC across all ROI’s and analyses. Thus, we show MTL method provides reliable striatal subregion segmentations.
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