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Abstract #4264

CAST: A multi-scale Convolutional neural network based Automated hippocampal subfield Segmentation Toolbox

Zhengshi Yang1, Xiaowei Zhuang1, Karthik Sreenivasan1, Virendra Mishra1, and Dietmar Cordes1,2
1Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States, 2Department of Psychology and Neuroscience, University of Colorado, Boulder, CO, United States

The segmentation of human hippocampal subfields on in vivo MRI has gained great interest in the last decade, because these anatomic subregions were found to be highly specialized in recent studies and are potentially affected differentially by normal aging, Alzheimer’s disease, schizophrenia, epilepsy, major depressive disorder, and posttraumatic stress disorder. However, manually segmenting hippocampal subfields is labor-intensive and time-consuming, which limits the study to a small sample size. We developed a multi-scale Convolutional neural network based Automated hippocampal subfield Segmentation Toolbox (CAST) for automated segmentation, which can be easily trained and output segmented images in one minute.

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