@inproceedings{50953a241565437aaf1cf508c5a54060,
title = "Emphysema quantification on cardiac CT scans using hidden markov measure field model: The MESA lung study",
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.",
author = "Jie Yang and Angelini, {Elsa D.} and Balte, {Pallavi P.} and Hoffman, {Eric A.} and Wu, {Colin O.} and Venkatesh, {Bharath A.} and Barr, {R. Graham} and Laine, {Andrew F.}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.",
year = "2016",
doi = "10.1007/978-3-319-46723-8_72",
language = "English (US)",
isbn = "9783319467221",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "624--631",
editor = "Gozde Unal and Sebastian Ourselin and Leo Joskowicz and Sabuncu, {Mert R.} and William Wells",
booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings",
}