Label-Driven Brain Deformable Registration Using Structural Similarity and Nonoverlap Constraints

Shunbo Hu, Lintao Zhang, Yan Xu, Dinggang Shen

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

Abstract

Accurate deformable image registration is important for brain analysis. However, there are two challenges in deformation registration of brain magnetic resonance (MR) images. First, the global cerebrospinal fluid (CSF) regions are rarely aligned since most of them are located in narrow regions outside of gray matter (GM) tissue. Second, the small complex morphological structures in tissues are rarely aligned since dense deformation fields are too blurred. In this work, we use a weakly supervised registration scheme, which is driven by global segmentation labels and local segmentation labels via two special loss functions. Specifically, multiscale double Dice similarity is used to maximize the overlap of the same labels and also minimize the overlap of regions with different labels. The structural similarity loss function is further used to enhance registration performance of small structures, thus enhancing the whole image registration accuracy. Experimental results on inter-subject registration of T1-weighted MR brain images from the OASIS-1 dataset show that the proposed scheme achieves higher accuracy on CSF, GM and white matter (WM) compared with the baseline learning model.

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
Pages210-219
Number of pages10
ISBN (Print)9783030598600
DOIs
Publication statusPublished - 2020
Externally publishedYes
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

  • Deformable registration
  • Label-driven learning
  • MR brain images
  • Structural similarity

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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