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Abstract #5429

Automated Extraction of the Fetal Brain from Functional MRI Data

Saige E Rutherford1, Mike Angstad1, Jasmine Hect2, Andre Zapico1, Moriah Thomason2, and Chandra Sripada1

1Psychiatry, University of Michigan, Ann Arbor, MI, United States, 2Wayne State University, Detroit, MI, United States

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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