Efficient groupwise registration of MR brain images via Hierarchical graph set Shrinkage

Pei Dong, Xiaohuan Cao, Pew Thian Yap, Dinggang Shen

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

Abstract

Accurate and efficient groupwise registration is important for population analysis. Current groupwise registration methods suffer from high computational cost, which hinders their application to large image datasets. To alleviate the computational burden while delivering accurate groupwise registration result, we propose to use a hierarchical graph set to model the complex image distribution with possibly large anatomical variations, and then turn the groupwise registration problem as a series of simple-to-solve graph shrinkage problems. Specifically, first, we divide the input images into a set of image clusters hierarchically, where images within each image cluster have similar anatomical appearances whereas images falling into different image clusters have varying anatomical appearances. After clustering, two types of graphs, i.e., intra-graph and inter-graph, are employed to hierarchically model the image distribution both within and across the image clusters. The constructed hierarchical graph set divides the registration problem of the whole image set into a series of simple-to-solve registration problems, where the entire registration process can be solved accurately and efficiently. The final deformation pathway of each image to the estimated population center can be obtained by composing each part of the deformation pathway along the hierarchical graph set. To evaluate our proposed method, we performed registration of a hundred of brain images with large anatomical variations. The results indicate that our method yields significant improvement in registration performance over state-of-the-art groupwise registration methods.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
EditorsJulia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, Gabor Fichtinger, Alejandro F. Frangi
PublisherSpringer Verlag
Pages819-826
Number of pages8
ISBN (Print)9783030009274
DOIs
Publication statusPublished - 2018 Jan 1
Externally publishedYes
Event21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: 2018 Sep 162018 Sep 20

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11070 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
CountrySpain
CityGranada
Period18/9/1618/9/20

    Fingerprint

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Dong, P., Cao, X., Yap, P. T., & Shen, D. (2018). Efficient groupwise registration of MR brain images via Hierarchical graph set Shrinkage. In J. A. Schnabel, C. Davatzikos, C. Alberola-López, G. Fichtinger, & A. F. Frangi (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings (pp. 819-826). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11070 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-00928-1_92