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Abstract #1070

Fetal Brain Automatic Segmentation Using 3D Deep Convolutional Neural Network

Li Zhao1, Xue Feng2, Craig Meyer2, Yao Wu1, Adre J. du Plessis3, and Catherine Limperopoulos1

1Diagnostic Imaging and Radiology, Childrens National Medical Center, Washington, DC, United States, 2Biomedical Engineering, University of Virginia, Charlottesville, VA, United States, 3Fetal Medicine, Childrens National Medical Center, Washington, DC, United States

Fetal brain MR image segmentation is necessary for brain development research. Currently, this task mainly relies on labor-intensive manually contouring or correction, because automatic segmentation often fails due to the low image quality. In this work, we apply a convolutional neural network, 3D U-Net, to segment the fetal brain regions. The proposed method was validated on 209 fetal brain MRI scans, including healthy fetal controls and high-risk fetuses with congenital heart disease. The proposed method showed high consistency with the manual correction results and may facilitate the identification of aberrant fetal brain development by providing quantitative morphological information.

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