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Abstract #3481

Learning 4D Probabilistic Atlas of Fetal Brain with Multi-channel Registration Network

Yuchen Pei1, Fenqiang Zhao1, Liangjun Chen1, Zhengwang Wu1, Tao Zhong1, Ya Wang1, Li Wang1, He Zhang2, and Gang Li1
1Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA, Chapel Hill, NC, United States, 2Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China, Shanghai, China

Brain atlases are of fundamental importance for analyzing the dynamic neurodevelopment in fetal brains. Since the brain size, shape, and structure change rapidly during the prenatal development, it is essential to construct a spatiotemporal (4D) atlas with tissue probability maps for accurately characterizing dynamic changes in fetal brains and providing tissue prior for segmentation of fetal brain MR images. We propose a novel unsupervised learning framework for building multi-channel atlases by incorporating tissue segmentation. Based on 98 healthy fetuses from 22 to 36 weeks, the learned 4D fetal brain atlas includes intensity templates, corresponding tissue probability maps and parcellation maps.

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