Automatic hippocampus labeling using the hierarchy of sub-region random forests

Lichi Zhang, Qian Wang, Yaozong Gao, Guorong Wu, Dinggang Shen

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

Abstract

In this paper, we propose a multi-atlas-based framework for labeling hippocampus regions in the MR images. Our work aims at extending the random forests techniques for better performance, which contains two novel contributions: First, we design a novel strategy for training forests, to ensure that each forest is specialized in labeling the certain sub-region of the hippocampus in the images. In the testing stage, a novel approach is also presented for automatically finding the forests relevant to the corresponding sub-regions of the test image. Second, we present a novel localized registration strategy, which further reduces the shape variations of the hippocampus region in each atlas. This can provide better support for the proposed sub-region random forest approach. We validate the proposed framework on the ADNI dataset, in which atlases from NC, MCI and AD subjects are randomly selected for the experiments. The estimations demonstrated the validity of the proposed framework, showing that it yields better performances than the conventional random forests techniques.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages19-27
Number of pages9
Volume9467
ISBN (Print)9783319281933
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event1st International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2015 - Munich, Germany
Duration: 2015 Oct 92015 Oct 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9467
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other1st International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2015
CountryGermany
CityMunich
Period15/10/915/10/9

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Zhang, L., Wang, Q., Gao, Y., Wu, G., & Shen, D. (2015). Automatic hippocampus labeling using the hierarchy of sub-region random forests. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9467, pp. 19-27). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9467). Springer Verlag. https://doi.org/10.1007/978-3-319-28194-0_3