Explaining radiological emphysema subtypes with unsupervised texture prototypes: MESA COPD study

Jie Yang, Elsa D. Angelini, Benjamin M. Smith, John H.M. Austin, Eric A. Hoffman, David A. Bluemke, R. Graham Barr, Andrew F. Laine

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

Abstract

Pulmonary emphysema is traditionally subcategorized into three subtypes, which have distinct radiological appearances on computed tomography (CT) and can help with the diagnosis of chronic obstructive pulmonary disease (COPD). Automated texture-based quantification of emphysema subtypes has been successfully implemented via supervised learning of these three emphysema subtypes. In this work, we demonstrate that unsupervised learning on a large heterogeneous database of CT scans can generate texture prototypes that are visually homogeneous and distinct, reproducible across subjects, and capable of predicting accurately the three standard radiological subtypes. These texture prototypes enable automated labeling of lung volumes, and open the way to new interpretations of lung CT scans with finer subtyping of emphysema.

Original languageEnglish (US)
Title of host publicationMedical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers
EditorsTal Arbel, Georg Langs, Mark Jenkinson, Bjoern Menze, William M. Wells III, Albert C.S. Chung, B. Michael Kelm, Weidong Cai, Albert Montillo, Dimitris Metaxas, M. Jorge Cardoso, Shaoting Zhang, Annemie Ribbens, Henning Muller
PublisherSpringer Verlag
Pages69-80
Number of pages12
ISBN (Print)9783319611877
DOIs
StatePublished - 2017
EventInternational Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece
Duration: Oct 21 2016Oct 21 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10081 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
CountryGreece
CityAthens
Period10/21/1610/21/16

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Yang, J., Angelini, E. D., Smith, B. M., Austin, J. H. M., Hoffman, E. A., Bluemke, D. A., Barr, R. G., & Laine, A. F. (2017). Explaining radiological emphysema subtypes with unsupervised texture prototypes: MESA COPD study. In T. Arbel, G. Langs, M. Jenkinson, B. Menze, W. M. Wells III, A. C. S. Chung, B. M. Kelm, W. Cai, A. Montillo, D. Metaxas, M. J. Cardoso, S. Zhang, A. Ribbens, & H. Muller (Eds.), Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers (pp. 69-80). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10081 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-61188-4_7