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Columbia University’s Data Science Institute Presents:

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Wednesday, April 6 • 2:00pm - 4:30pm
Adaptive Quantification of Pulmonary Emphysema With a Hidden Markov Measure Field Model [P1]

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Full-lung (FL) computed tomography (CT) has been widely used for pulmonary emphysema quantification. Cardiac CT scans include approximately 2/3 of the lung and can be obtained more rapidly and with lower radiation exposure than FL scans. This work presents a novel method for emphysema quantification on both FL and cardiac scans, based on parametric modeling of intensity distributions and a hidden Markov measure field model to segment emphysematous regions. The framework adapts to the characteristics of an image to ensure a robust quantification of emphysema under varying CT imaging protocols and scanner types. Compared to standard approaches, the presented model provides 1) very high longitudinal correlation with 87 subjects and a total of 365 FL scans acquired with varying imaging protocols; and 2) more reproducible emphysema segmentation and higher longitudinal correlations from a diverse pool of scanner types and thousands of subjects with ten thousands of cardiac scans.

Demo/Poster Presenter
avatar for Jie Yang

Jie Yang

PhD Candidate in Biomedical Engineering, Columbia Engineering
Jie Yang is a Ph.D. student in the Department of Biomedical Engineering at Columbia University, working with Dr. Andrew Laine. Her research interests are in quantitative medical image analysis and pattern recognition. She is currently working on adaptive pulmonary emphysema quantification and texture learning from computed tomography (CT) images of the lung.

Wednesday April 6, 2016 2:00pm - 4:30pm
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040