Early detection of emphysema progression

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Emphysema is one of the most widespread diseases in subjects with smoking history. The gold standard method for estimating the severity of emphysema is a lung function test, such as forced expiratory volume in first second (FEV1). However, several clinical studies showed that chest CT scans offer more sensitive estimates of emphysema progression. The standard CT densitometric score of emphysema is the relative area of voxels below a threshold (RA). The RA score is a global measurement and reflects the overall emphysema progression. In this work, we propose a framework for estimation of local emphysema progression from longitudinal chest CT scans. First, images are registered to a common system of coordinates and then local image dissimilarities are computed in corresponding anatomical locations. Finally, the obtained dissimilarity representation is converted into a single emphysema progression score. We applied the proposed algorithm on 27 patients with severe emphysema with CT scans acquired five time points, at baseline, after 3, after 12, after 21 and after 24 or 30 months. The results showed consistent emphysema progression with time and the overall progression score correlates significantly with the increase in RA score.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2010 : 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part II
EditorsTianzi Jiang, Nassir Navab, Josien P. W. Pluim, Max A. Viergever
Number of pages8
VolumePart II
Publication date2010
ISBN (Print)978-3-642-15744-8
ISBN (Electronic)978-3-642-15745-5
Publication statusPublished - 2010
Event13th International Conference on Medical Image Computing and Computer Assisted Intervention - Beijing, China
Duration: 20 Sep 201024 Sep 2010
Conference number: 13


Conference13th International Conference on Medical Image Computing and Computer Assisted Intervention
SeriesLecture notes in computer science

ID: 21235859