Automated estimation of progression of interstitial lung disease in CT images.
Publication year
2010Source
Medical Physics, 37, 1, (2010), pp. 63-73ISSN
Annotation
01 januari 2010
Publication type
Article / Letter to editor
![https://hdl.handle.net/2066/88430](https://cdn.statically.io/img/repository.ubn.ru.nl/themes/Mirage2//images/copy.png)
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Organization
Radiology
Journal title
Medical Physics
Volume
vol. 37
Issue
iss. 1
Page start
p. 63
Page end
p. 73
Subject
N4i 4: Auto-immunity, transplantation and immunotherapy; Medical Imaging - Radboud University Medical CenterAbstract
PURPOSE: A system is presented for automated estimation of progression of interstitial lung disease in serial thoracic CT scans. METHODS: The system compares corresponding 2D axial sections from baseline and follow-up scans and concludes whether this pair of sections represents regression, progression, or unchanged disease status. The correspondence between serial CT scans is achieved by intrapatient volumetric image registration. The system classification function is trained with two different feature sets. Features in the first set represent the intensity distribution of a difference image between the baseline and follow-up CT sections. Features in the second set represent dissimilarities computed between the baseline and follow-up images filtered with a bank of general purpose texture filters. RESULTS: In an experiment on 74 scan pairs, the system classification accuracies were 76.1% and 79.5% for the two feature sets, respectively, while the accuracies of two observer radiologist were 78.5% and 82%, respectively. The agreements of the system with the reference standard, measured by weighted kappa statistics, were 0.611 and 0.683 for the two feature sets, respectively. CONCLUSIONS: The system employing the second feature set showed good agreement with the reference standard, and its accuracy approached that of two radiologists.
This item appears in the following Collection(s)
- Academic publications [241901]
- Electronic publications [127360]
- Faculty of Medical Sciences [91954]
- Open Access publications [102119]
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