In this study, we present a novel application of a Convolution Neural Network algorithm to a challenging image segmentation problem: fetal brain segmentation. Resting-state fMRI data was obtained from 192 fetuses (gestational age 20-40 weeks, M=31.9, SD=4.28). The output from automated extractions are compared with the ground truth of manually drawn brain masks. We report that automated fetal brain localization and extraction is achievable at the same integrity of manual methods, in a fraction of the time.
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