%0 Journal Article
%T Automatic brain extraction from 3D fetal MR image with deep learning-based multi-step framework.
%A Chen J
%A Fang Z
%A Zhang G
%A Ling L
%A Li G
%A Zhang H
%A Wang L
%J Comput Med Imaging Graph
%V 88
%N 0
%D 03 2021
%M 33385932
%F 7.422
%R 10.1016/j.compmedimag.2020.101848
%X Brain extraction is a fundamental prerequisite step in neuroimage analysis for fetus. Due to surrounding maternal tissues and unpredictable movement, brain extraction from fetal Magnetic Resonance (MR) images is a challenging task. In this paper, we propose a novel deep learning-based multi-step framework for brain extraction from 3D fetal MR images. In the first step, a global localization network is applied to estimate probability maps for brain candidates. Connected-component labeling algorithm is applied to eliminate small erroneous components and accurately locate the candidate brain area. In the second step, a local refinement network is implemented in the brain candidate area to obtain fine-grained probability maps. Final extraction results are derived by a fusion network with the two cascaded probability maps obtained from previous two steps. Experimental results demonstrate that our proposed method has superior performance compared with existing deep learning-based methods.