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Abstract #4797

Automated Fetal Brain Segmentation Using Deep Convolutional Neural Network

Bin Chen1, Liming Wu1, Bing Zhang2, Simin Liu3, and Hua Guo3

1Purdue University Northwest, Hammond, IN, United States, 2Nanjing University Medical School, Nanjing, China, 3Tsinghua University, Beijing, China

Recent advances show promising fetal brain reconstruction results through image motion correction and super resolution from a stack of unregistered images consisting of in-plane motion free snapshot slices acquired by fast imaging methods. Most motion correction and super resolution techniques for 3D volume reconstruction require accurate fetal brain segmentation as the first step of image analysis. In this study, a customized U-Net based deep learning method was implemented for automatic fetal brain segmentation. The high accuracy of deep learning based semantic segmentation improves the performance in volume registration as well as quantitative studies of brain development and group analysis.

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