Automatic method for thalamus parcellation using multi-modal feature classification

Joshua V. Stough, Jeffrey Glaister, Chuyang Ye, Sarah H. Ying, Jerry L. Prince, Aaron Carass

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

Abstract

Segmentation and parcellation of the thalamus is an important step in providing volumetric assessment of the impact of disease on brain structures. Conventionally, segmentation is carried out on T1-weighted magnetic resonance (MR) images and nuclear parcellation using diffusion weighted MR images. We present the first fully automatic method that incorporates both tissue contrasts and several derived features to first segment and then parcellate the thalamus. We incorporate fractional anisotrophy, fiber orientation from the 5D Knutsson representation of the principal eigenvectors, and connectivity between the thalamus and the cortical lobes, as features. Combining these multiple information sources allows us to identify discriminating dimensions and thus parcellate the thalamic nuclei. A hierarchical random forest framework with a multidimensional feature per voxel, first distinguishes thalamus from background, and then separates each group of thalamic nuclei. Using a leave one out cross-validation on 12 subjects we have a mean Dice score of 0.805 and 0.799 for the left and right thalami, respectively. We also report overlap for the thalamic nuclear groups.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings
PublisherSpringer Verlag
Pages169-176
Number of pages8
EditionPART 3
ISBN (Print)9783319104423
DOIs
StatePublished - Jan 1 2014
Event17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - Boston, MA, United States
Duration: Sep 14 2014Sep 18 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume8675 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014
CountryUnited States
CityBoston, MA
Period9/14/149/18/14

    Fingerprint

Keywords

  • Brain imaging
  • diffusion MRI
  • machine learning
  • magnetic resonance imaging
  • segmentation
  • thalamus parcellation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Stough, J. V., Glaister, J., Ye, C., Ying, S. H., Prince, J. L., & Carass, A. (2014). Automatic method for thalamus parcellation using multi-modal feature classification. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings (PART 3 ed., pp. 169-176). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8675 LNCS, No. PART 3). Springer Verlag. https://doi.org/10.1007/978-3-319-10443-0_22