Extraction of airway trees using multiple hypothesis tracking and template matching

Knowledge of airway tree morphology has important clinical applications in diagnosis of chronic obstructive pulmonary disease. We present an automatic tree extraction method based on multiple hypothesis tracking and template matching for this purpose and evaluate its performance on chest CT images. The method is adapted from a semi-automatic method devised for vessel segmentation. Idealized tubular templates are constructed that match airway probability obtained from a trained classifier and ranked based on their relative significance. Several such regularly spaced templates form the local hypotheses used in constructing a multiple hypothesis tree, which is then traversed to reach decisions. The proposed modifications remove the need for local thresholding of hypotheses as decisions are made entirely based on statistical comparisons involving the hypothesis tree. The results show improvements in performance when compared to the original method and region growing on intensity images. We also compare the method with region growing on the probability images, where the presented method does not show substantial improvement, but we expect it to be less sensitive to local anomalies in the data.Comment: 12 pages. Presented at the MICCAI Pulmonary Image Analysis Workshop, Athens, Greece, 201

Transfer learning for multicenter classification of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a lung disease which can be quantified using chest computed tomography (CT) scans. Recent studies have shown that COPD can be automatically diagnosed using weakly supervised learning of intensity and texture distributions. However, up till now such classifiers have only been evaluated on scans from a single domain, and it is unclear whether they would generalize across domains, such as different scanners or scanning protocols. To address this problem, we investigate classification of COPD in a multi-center dataset with a total of 803 scans from three different centers, four different scanners, with heterogenous subject distributions. Our method is based on Gaussian texture features, and a weighted logistic classifier, which increases the weights of samples similar to the test data. We show that Gaussian texture features outperform intensity features previously used in multi-center classification tasks. We also show that a weighting strategy based on a classifier that is trained to discriminate between scans from different domains, can further improve the results. To encourage further research into transfer learning methods for classification of COPD, upon acceptance of the paper we will release two feature datasets used in this study on http://bigr.nl/research/projects/copdComment: Accepted at Journal of Biomedical and Health Informatic

Implementation and organization of lung cancer screening

CT screening for lung cancer is now being implemented in the US and China on a widespread national scale but not in Europe so far. The review gives a status for the implementation process and the hurdles to overcome in the future. It also describes the guidelines and requirements for the structure and components of high quality CT screening programs. These are essential in order to achieve a successful program with the fewest possible harms and a possible mortality benefit like that documented in the American National Lung Screening Trial (NLST). In addition the importance of continued research in CT screening methods is described and discussed with focus on the great potential to further improve this method in the future for the benefit of patients and society

Smoking cessation and lung cancer screening

Smoking behavior may have a substantial influence on the overall effect of lung cancer screening. Non-randomized studies of smoking behavior during screening have indicated that computer tomography (CT) screening induces smoking cessation. Randomized studies have further elaborated that this effect has to do with participation in screening alone and not dependent on the CT scan. Participants in both CT and control arm in randomized screening trials had higher smoking abstinence rate compared to that of the general population. A positive screening test seems to further promote smoking cessation and decrease smoking relapse rate. Also low smoking dependency and high motivation to quit smoking at baseline predicted smoking abstinence in screening trials. Lung cancer screening therefore seems to be a teachable moment for smoking cessation. Targeted smoking cessation counselling should be an integrated part of future lung cancer screening trials