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Abstract #4570

3D Fetal Brain Segmentation using an Optimized Deep Learning Approach

Li Zhao1, Xue Feng2, Josepheen Asis-Cruz 1, Yao Wu1, Kushal Kapse1, Axel Ludwig1, Dan Wu3, Kun Qing2, Carig H. Meyer2, and Catherine Limperopoulos1
1Childrens National Hospital, Washington, DC, United States, 2University of Virginia, Charlottesville, VA, United States, 3Zhejiang University, Hanzhou, China

An essential step to accurately quantify fetal brain development is to reliably segment brain regions and perform volumetric measurements. However, this task mainly relies on labor intensive manually contouring. In this work, a 3D U-Net method was optimized and evaluated for fetal brain segmentation. 3D U-Net and 4D atlas-based segmentation methods were compared on 46 fetal brain MRI scans with gestational age 26.4 to 39.1 weeks. The proposed method resulted in (1) higher consistency with the manual segmentation, (2) shorter processing time, and (3) more consistent results across gestational ages, compared to a 4D atlas-based method.

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