Ansatte – Københavns Universitet

Quantifying emphysema extent from weakly labeled CT scans of the lungs using label proportions learning

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedings

Dokumenter

Silas Nyboe Ørting, Jens Petersen, Mathilde Wille, Laura Thomsen, Marleen de Bruijne

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.
OriginalsprogEngelsk
TitelThe Sixth International Workshop on Pulmonary Image Analysis
RedaktørerReinhard R. Beichel, Keyvan Farahani, Colin Jacobs, Sven Kabus, Atilla P. Kiraly, Jan-Martin Kuhnigk, Jamie R. McClelland, Kensaku Mori, Jens Petersen, Simon Rit
Antal sider11
ForlagCreateSpace Independent Publishing Platform
Publikationsdato2016
Sider31-42
ISBN (Trykt)978-1537038582
StatusUdgivet - 2016
BegivenhedSixth International Workshop on Pulmonary Image Analysis - Athen, Grækenland
Varighed: 21 okt. 201621 okt. 2016
Konferencens nummer: 6

Konference

KonferenceSixth International Workshop on Pulmonary Image Analysis
Nummer6
LandGrækenland
ByAthen
Periode21/10/201621/10/2016

Antal downloads er baseret på statistik fra Google Scholar og www.ku.dk


Ingen data tilgængelig

ID: 167582102