Automatic segmentation of hippocampus for longitudinal infant brain MR image sequence by spatial-temporal hypergraph learning

Yanrong Guo, Pei Dong, Shijie Hao, Li Wang, Guorong Wu, Dinggang Shen

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

1 Citation (Scopus)

Abstract

Accurate segmentation of infant hippocampus from Magnetic Resonance (MR) images is one of the key steps for the investigation of early brain development and neurological disorders. Since the manual delineation of anatomical structures is time-consuming and irreproducible, a number of automatic segmentation methods have been proposed, such as multi-atlas patch-based label fusion methods. However, the hippocampus during the first year of life undergoes dynamic appearance, tissue contrast and structural changes, which pose substantial challenges to the existing label fusion methods. In addition, most of the existing label fusion methods generally segment target images at each time-point independently, which is likely to result in inconsistent hippocampus segmentation results along different time-points. In this paper, we treat a longitudinal image sequence as a whole, and propose a spatial-temporal hypergraph based model to jointly segment infant hippocampi from all time-points. Specifically, in building the spatial-temporal hypergraph, (1) the atlas-to-target relationship and (2) the spatial/temporal neighborhood information within the target image sequence are encoded as two categories of hyperedges. Then, the infant hippocampus segmentation from the whole image sequence is formulated as a semi-supervised label propagation model using the proposed hypergraph. We evaluate our method in segmenting infant hippocampi from T1-weighted brain MR images acquired at the age of 2 weeks, 3 months, 6 months, 9 months, and 12 months. Experimental results demonstrate that, by leveraging spatial-temporal information, our method achieves better performance in both segmentation accuracy and consistency over the state-of-the-art multi-atlas label fusion 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
EditorsPierrick Coupe, Brent C. Munsell, Daniel Rueckert, Yiqiang Zhan, Guorong Wu
PublisherSpringer Verlag
Pages1-8
Number of pages8
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)0302-9743
ISSN (Electronic)1611-3349

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Guo, Y., Dong, P., Hao, S., Wang, L., Wu, G., & Shen, D. (2016). Automatic segmentation of hippocampus for longitudinal infant brain MR image sequence by spatial-temporal hypergraph learning. In P. Coupe, B. C. Munsell, D. Rueckert, Y. Zhan, & G. Wu (Eds.), Patch-Based Techniques in Medical Imaging - 2nd International Workshop, Patch-MI 2016 held in conjunction with MICCAI 2016, Proceedings (pp. 1-8). (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_1