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Mapping fetal brain development based on automated segmentation and 4D brain atlasing

By Haotian Li, Guohui Yan, Wanrong Luo, Tintin Liu, Yan Wang, Ruibin Liu, Weihao Zheng, Yi Zhang, Kui Li, Li Zhao, Catherine Limperopoulos, Yu Zou, Dan Wu

Posted 11 May 2020
bioRxiv DOI: 10.1101/2020.05.10.085381

Fetal brain MRI has become an important tool for in utero assessment of brain development and disorders. However, quantitative analysis of fetal brain MRI remains difficult, partially due to the limited tools for automated preprocessing and the lack of normative brain templates. In this paper, we proposed an automated pipeline for fetal brain extraction, super-resolution reconstruction, and fetal brain atlasing to quantitatively map in utero fetal brain development during mid-to-late gestation in a Chinese population. First, we designed a U-net convolutional neural network for automated fetal brain extraction, which achieved an average accuracy of 97%. We then generated a developing fetal brain atlas, using an iterative linear and nonlinear registration approach. Based on the 4D spatiotemporal atlas, we quantified the morphological development of the fetal brain between 23-36 weeks of gestation. The proposed pipeline enabled the fully-automated volumetric reconstruction for clinically available fetal brain MRI data, and the 4D fetal brain atlas provided normative templates for quantitative analysis of potential fetal brain abnormalities, especially in the Chinese population. ### Competing Interest Statement The authors have declared no competing interest.

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