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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