Consistent multi-atlas hippocampus segmentation for longitudinal MR brain images with temporal sparse representation

Lin Wang, Yanrong Guo, Xiaohuan Cao, Guorong Wu, Dinggang Shen

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

3 Citations (Scopus)

Abstract

In this paper, we propose a novel multi-atlas based longitudinal label fusion method with temporal sparse representation technique to segment hippocampi at all time points simultaneously. First, we use groupwise longitudinal registration to simultaneously (1) estimate a group-mean image of a subject image sequence and (2) register its all time-point images to the estimated groupmean image consistently over time. Then, by registering all atlases with the group-mean image, we can align all atlases longitudinally consistently to each time point of the subject image sequence. Finally, we propose a longitudinal label fusion method to propagate all atlas labels to the subject image sequence by simultaneously labeling a set of temporally-corresponded voxels with a temporal consistency constraint on sparse representation. Experimental results demonstrate that our proposed method can achieve more accurate and consistent hippocampus segmentation than the state-of-the-art counterpart methods.

Original languageEnglish
Title of host publicationPatch-Based Techniques in Medical Imaging - 2nd International Workshop, Patch-MI 2016 held in conjunction with MICCAI 2016, Proceedings
PublisherSpringer Verlag
Pages34-42
Number of pages9
Volume9993 LNCS
ISBN (Print)9783319471174
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event2nd International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2016 - Athens, Greece
Duration: 2016 Oct 172016 Oct 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9993 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other2nd International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2016
CountryGreece
CityAthens
Period16/10/1716/10/17

Fingerprint

Hippocampus
Sparse Representation
Atlas
Labels
Brain
Segmentation
Image Sequence
Fusion reactions
Fusion
Labeling
Voxel
Registration
Experimental Results
Estimate
Demonstrate

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Wang, L., Guo, Y., Cao, X., Wu, G., & Shen, D. (2016). Consistent multi-atlas hippocampus segmentation for longitudinal MR brain images with temporal sparse representation. In Patch-Based Techniques in Medical Imaging - 2nd International Workshop, Patch-MI 2016 held in conjunction with MICCAI 2016, Proceedings (Vol. 9993 LNCS, pp. 34-42). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9993 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-47118-1_5

Consistent multi-atlas hippocampus segmentation for longitudinal MR brain images with temporal sparse representation. / Wang, Lin; Guo, Yanrong; Cao, Xiaohuan; Wu, Guorong; Shen, Dinggang.

Patch-Based Techniques in Medical Imaging - 2nd International Workshop, Patch-MI 2016 held in conjunction with MICCAI 2016, Proceedings. Vol. 9993 LNCS Springer Verlag, 2016. p. 34-42 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9993 LNCS).

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

Wang, L, Guo, Y, Cao, X, Wu, G & Shen, D 2016, Consistent multi-atlas hippocampus segmentation for longitudinal MR brain images with temporal sparse representation. in Patch-Based Techniques in Medical Imaging - 2nd International Workshop, Patch-MI 2016 held in conjunction with MICCAI 2016, Proceedings. vol. 9993 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9993 LNCS, Springer Verlag, pp. 34-42, 2nd International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2016, Athens, Greece, 16/10/17. https://doi.org/10.1007/978-3-319-47118-1_5
Wang L, Guo Y, Cao X, Wu G, Shen D. Consistent multi-atlas hippocampus segmentation for longitudinal MR brain images with temporal sparse representation. In Patch-Based Techniques in Medical Imaging - 2nd International Workshop, Patch-MI 2016 held in conjunction with MICCAI 2016, Proceedings. Vol. 9993 LNCS. Springer Verlag. 2016. p. 34-42. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-47118-1_5
Wang, Lin ; Guo, Yanrong ; Cao, Xiaohuan ; Wu, Guorong ; Shen, Dinggang. / Consistent multi-atlas hippocampus segmentation for longitudinal MR brain images with temporal sparse representation. Patch-Based Techniques in Medical Imaging - 2nd International Workshop, Patch-MI 2016 held in conjunction with MICCAI 2016, Proceedings. Vol. 9993 LNCS Springer Verlag, 2016. pp. 34-42 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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