Quantifying emphysema extent from weakly labeled CT scans of the lungs using label proportions learning
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Quantifying emphysema extent from weakly labeled CT scans of the lungs using label proportions learning. / Ørting, Silas Nyboe; Petersen, Jens; Wille, Mathilde; Thomsen, Laura; de Bruijne, Marleen.
The Sixth International Workshop on Pulmonary Image Analysis. ed. / Reinhard R. Beichel; Keyvan Farahani; Colin Jacobs; Sven Kabus; Atilla P. Kiraly; Jan-Martin Kuhnigk; Jamie R. McClelland; Kensaku Mori; Jens Petersen; Simon Rit. CreateSpace Independent Publishing Platform , 2016. p. 31-42.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Quantifying emphysema extent from weakly labeled CT scans of the lungs using label proportions learning
AU - Ørting, Silas Nyboe
AU - Petersen, Jens
AU - Wille, Mathilde
AU - Thomsen, Laura
AU - de Bruijne, Marleen
N1 - Conference code: 6
PY - 2016
Y1 - 2016
N2 - Quantification of emphysema extent is important in diagnosing and monitoring patients with chronic obstructive pulmonary disease (COPD). Several studies have shown that emphysema quantification by supervised texture classification is more robust and accurate than traditional densitometry. Current techniques require highly time consuming manual annotations of patches or use only weak labels indicating overall disease status (e.g, COPD or healthy). We show how visual scoring of regional emphysema extent can be exploited in a learning with label proportions (LLP) framework to both predict presence of emphysema in smaller patches and estimate regional extent. We evaluate performance on 195 visually scored CT scans and achieve an intraclass correlation of 0.72 (0.65–0.78) between predicted region extent and expert raters. To our knowledge this is the first time that LLP methods have been applied to medical imaging data.
AB - Quantification of emphysema extent is important in diagnosing and monitoring patients with chronic obstructive pulmonary disease (COPD). Several studies have shown that emphysema quantification by supervised texture classification is more robust and accurate than traditional densitometry. Current techniques require highly time consuming manual annotations of patches or use only weak labels indicating overall disease status (e.g, COPD or healthy). We show how visual scoring of regional emphysema extent can be exploited in a learning with label proportions (LLP) framework to both predict presence of emphysema in smaller patches and estimate regional extent. We evaluate performance on 195 visually scored CT scans and achieve an intraclass correlation of 0.72 (0.65–0.78) between predicted region extent and expert raters. To our knowledge this is the first time that LLP methods have been applied to medical imaging data.
M3 - Article in proceedings
SN - 978-1537038582
SP - 31
EP - 42
BT - The Sixth International Workshop on Pulmonary Image Analysis
A2 - Beichel, Reinhard R.
A2 - Farahani, Keyvan
A2 - Jacobs, Colin
A2 - Kabus, Sven
A2 - Kiraly, Atilla P.
A2 - Kuhnigk, Jan-Martin
A2 - McClelland, Jamie R.
A2 - Mori, Kensaku
A2 - Petersen, Jens
A2 - null, Simon Rit
PB - CreateSpace Independent Publishing Platform
Y2 - 21 October 2016 through 21 October 2016
ER -
ID: 167582102