Unsupervised Discovery of Emphysema Subtypes in a Large Clinical Cohort
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nihms837319.pdf
Description
Accepted version
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583.37 KB
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Author(s)
Binder, Polina
Batmanghelich, Nematollah K.
Estepar, Raul San Jose
Golland, Polina
Date Issued
2016
Journal
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Citation
Binder, Polina et al. "Unsupervised Discovery of Emphysema Subtypes in a Large Clinical Cohort." MLMI 2016: Machine Learning in Medical Imaging, Lecture Notes in Computer Science, 10019, Springer International Publishing, 2016. © 2016 Springer International Publishing
Version
Author's final manuscript
Abstract
Emphysema is one of the hallmarks of Chronic Obstructive Pulmonary Disorder (COPD), a devastating lung disease often caused by smoking. Emphysema appears on Computed Tomography (CT) scans as a variety of textures that correlate with disease subtypes. It has been shown that the disease subtypes and textures are linked to physiological indicators and prognosis, although neither is well characterized clinically. Most previous computational approaches to modeling emphysema imaging data have focused on supervised classification of lung textures in patches of CT scans. In this work, we describe a generative model that jointly captures heterogeneity of disease subtypes and of the patient population. We also describe a corresponding inference algorithm that simultaneously discovers disease subtypes and population structure in an unsupervised manner. This approach enables us to create image-based descriptors of emphysema beyond those that can be identified through manual labeling of currently defined phenotypes. By applying the resulting algorithm to a large data set, we identify groups of patients and disease subtypes that correlate with distinct physiological indicators.
Description
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10019)
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-319-47157-0_22
