Automatic subcortical segmentation using a contextual model

Jonathan H. Morra, Zhuowen Tu, Liana G. Apostolova, Amity E. Green, Arthur W. Toga, Paul M. Thompson

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

28 Scopus citations

Abstract

Automatically segmenting subcortical structures in brain images has the potential to greatly accelerate drug trials and population studies of disease. Here we propose an automatic subcortical segmentation algorithm using the auto context model. Unlike many segmentation algorithms that separately compute a shape prior and an image appearance model, we develop a framework based on machine learning to learn a unified appearance and context model. We trained our algorithm to segment the hippocampus and tested it on 83 brain MRIs (of 35 Alzheimer's disease patients, 22 with mild cognitive impairment, and 26 normal healthy controls). Using standard distance and overlap metrics, the auto context model method significantly outperformed simpler learning-based algorithms (using AdaBoost alone) and the FreeSurfer system. In tests on a public domain dataset designed to validate segmentation [1] , our new algorithm also greatly improved upon a recently-proposed hybrid discriminative/generative approach [2], which was among the top three that performed comparably in a recent head-to-head competition.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings
Pages194-201
Number of pages8
EditionPART 1
DOIs
StatePublished - Dec 1 2008
Event11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008 - New York, NY, United States
Duration: Sep 6 2008Sep 10 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5241 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008
CountryUnited States
CityNew York, NY
Period9/6/089/10/08

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Morra, J. H., Tu, Z., Apostolova, L. G., Green, A. E., Toga, A. W., & Thompson, P. M. (2008). Automatic subcortical segmentation using a contextual model. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings (PART 1 ed., pp. 194-201). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5241 LNCS, No. PART 1). https://doi.org/10.1007/978-3-540-85988-8_24