Anatomy-Guided Convolutional Neural Network for Motion Correction in Fetal Brain MRI

Yuchen Pei, Lisheng Wang, Fenqiang Zhao, Tao Zhong, Lufan Liao, Dinggang Shen, Gang Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Fetal Magnetic Resonance Imaging (MRI) is challenged by the fetal movements and maternal breathing. Although fast MRI sequences allow artifact free acquisition of individual 2D slices, motion commonly occurs in between slices acquisitions. Motion correction for each slice is thus very important for reconstruction of 3D fetal brain MRI, but is highly operator-dependent and time-consuming. Approaches based on convolutional neural networks (CNNs) have achieved encouraging performance on prediction of 3D motion parameters of arbitrarily oriented 2D slices, which, however, does not capitalize on important brain structural information. To address this problem, we propose a new multi-task learning framework to jointly learn the transformation parameters and tissue segmentation map of each slice, for providing brain anatomical information to guide the mapping from 2D slices to 3D volumetric space in a coarse to fine manner. In the coarse stage, the first network learns the features shared for both regression and segmentation tasks. In the refinement stage, to fully utilize the anatomical information, distance maps constructed based on the coarse segmentation are introduced to the second network. Finally, incorporation of the signed distance maps to guide the regression and segmentation together improves the performance in both tasks. Experimental results indicate that the proposed method achieves superior performance in reducing the motion prediction error and obtaining satisfactory tissue segmentation results simultaneously, compared with state-of-the-art methods.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsMingxia Liu, Chunfeng Lian, Pingkun Yan, Xiaohuan Cao
PublisherSpringer Science and Business Media Deutschland GmbH
Pages384-393
Number of pages10
ISBN (Print)9783030598600
DOIs
Publication statusPublished - 2020
Event11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 2020 Oct 42020 Oct 4

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12436 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020
CountryPeru
CityLima
Period20/10/420/10/4

Keywords

  • Anatomical knowledge
  • Fetal brain
  • Motion correction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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