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Automatic brain extraction from 3D fetal MR image with deep learning-based multi-step framework.

Authors
  • Chen, Jian1
  • Fang, Zhenghan2
  • Zhang, Guofu3
  • Ling, Lei3
  • Li, Gang2
  • Zhang, He4
  • Wang, Li5
  • 1 School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, Fujian, 330118, China. Electronic address: [email protected] , (China)
  • 2 Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, 27517, USA.
  • 3 Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, 200011, China. , (China)
  • 4 Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, 200011, China. Electronic address: [email protected] , (China)
  • 5 Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, 27517, USA. Electronic address: [email protected]
Type
Published Article
Journal
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Publication Date
Dec 29, 2020
Volume
88
Pages
101848–101848
Identifiers
DOI: 10.1016/j.compmedimag.2020.101848
PMID: 33385932
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

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. Copyright © 2020 Elsevier Ltd. All rights reserved.

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