Emphysema quantification on cardiac CT scans using hidden markov measure field model: The MESA lung study

Jie Yang; Elsa D. Angelini; Pallavi P. Balte; Eric A. Hoffman; Colin O. Wu; Bharath A. Venkatesh; R. Graham Barr; Andrew F. Laine

doi:10.1007/978-3-319-46723-8_72

Emphysema quantification on cardiac CT scans using hidden markov measure field model: The MESA lung study

Jie Yang, Elsa D. Angelini, Pallavi P. Balte, Eric A. Hoffman, Colin O. Wu, Bharath A. Venkatesh, R. Graham Barr, Andrew F. Laine

School of Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Cardiac computed tomography (CT) scans include approximately 2/3 of the lung and can be obtained with low radiation exposure. Large cohorts of population-based research studies reported high correlations of emphysema quantification between full-lung (FL) and cardiac CT scans,using thresholding-based measurements. This work extends a hidden Markov measure field (HMMF) model-based segmentation method for automated emphysema quantification on cardiac CT scans. We show that the HMMF-based method,when compared with several types of thresholding,provides more reproducible emphysema segmentation on repeated cardiac scans,and more consistent measurements between longitudinal cardiac and FL scans from a diverse pool of scanner types and thousands of subjects with ten thousands of scans.

Original language	English (US)
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
Editors	Gozde Unal, Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells
Publisher	Springer Verlag
Pages	624-631
Number of pages	8
ISBN (Print)	9783319467221
DOIs	https://doi.org/10.1007/978-3-319-46723-8_72
State	Published - 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9901 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-46723-8_72

Cite this

Yang, J., Angelini, E. D., Balte, P. P., Hoffman, E. A., Wu, C. O., Venkatesh, B. A., Barr, R. G., & Laine, A. F. (2016). Emphysema quantification on cardiac CT scans using hidden markov measure field model: The MESA lung study. In G. Unal, S. Ourselin, L. Joskowicz, M. R. Sabuncu, & W. Wells (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (pp. 624-631). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9901 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46723-8_72

Emphysema quantification on cardiac CT scans using hidden markov measure field model : The MESA lung study. / Yang, Jie; Angelini, Elsa D.; Balte, Pallavi P. et al.

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. ed. / Gozde Unal; Sebastian Ourselin; Leo Joskowicz; Mert R. Sabuncu; William Wells. Springer Verlag, 2016. p. 624-631 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9901 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yang, J, Angelini, ED, Balte, PP, Hoffman, EA, Wu, CO, Venkatesh, BA, Barr, RG & Laine, AF 2016, Emphysema quantification on cardiac CT scans using hidden markov measure field model: The MESA lung study. in G Unal, S Ourselin, L Joskowicz, MR Sabuncu & W Wells (eds), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9901 LNCS, Springer Verlag, pp. 624-631. https://doi.org/10.1007/978-3-319-46723-8_72

Yang J, Angelini ED, Balte PP, Hoffman EA, Wu CO, Venkatesh BA et al. Emphysema quantification on cardiac CT scans using hidden markov measure field model: The MESA lung study. In Unal G, Ourselin S, Joskowicz L, Sabuncu MR, Wells W, editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Springer Verlag. 2016. p. 624-631. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-46723-8_72

Yang, Jie ; Angelini, Elsa D. ; Balte, Pallavi P. et al. / Emphysema quantification on cardiac CT scans using hidden markov measure field model : The MESA lung study. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. editor / Gozde Unal ; Sebastian Ourselin ; Leo Joskowicz ; Mert R. Sabuncu ; William Wells. Springer Verlag, 2016. pp. 624-631 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Cardiac computed tomography (CT) scans include approximately 2/3 of the lung and can be obtained with low radiation exposure. Large cohorts of population-based research studies reported high correlations of emphysema quantification between full-lung (FL) and cardiac CT scans,using thresholding-based measurements. This work extends a hidden Markov measure field (HMMF) model-based segmentation method for automated emphysema quantification on cardiac CT scans. We show that the HMMF-based method,when compared with several types of thresholding,provides more reproducible emphysema segmentation on repeated cardiac scans,and more consistent measurements between longitudinal cardiac and FL scans from a diverse pool of scanner types and thousands of subjects with ten thousands of scans.",

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Emphysema quantification on cardiac CT scans using hidden markov measure field model: The MESA lung study

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