Automated Image Analysis for the Characterization of Animal Models of Lung Disease using X-ray micro-Computed Tomography

Abstract

Lung cancer and chronic obstructive pulmonary disease (COPD) are among the deadliest diseases worldwide, and animal models play a key role in understanding the natural history of these diseases, as well as in pre-clinical treatment trials. Different techniques can be used to study animal models of lung disease, such as pulmonary function tests or histology. X-ray microcomputed tomography (micro-CT) represents a very convenient technology to obtain three-dimensional images of the lungs with minimum invasiveness. Multiple preliminary studies have shown the use of micro-CT to assess the progress of mouse models of lung disease. In this thesis, we set up a generic protocol for image acquisition which is of use even for heavily diseased animals. The protocol includes endotracheal intubation, pulmonary function tests and iso-pressure breath holds for movement artifact reduction. Chest micro-CT image segmentation and analysis methods have been developed to quantify the effects of disease. These methods allow for quantitative measurements on the lungs and the airways separately, which can be used to monitor disease development. Moreover, significant contributions have been made to the field of atlas-based segmentation, with applications in multiple image modalities and segmentation problems. Developed methods have been applied to characterize the dynamic evolution of three relevant mouse models of lung disease: elastase-induced emphysema, silica-induced chronic pulmonary inflammation and urethaneinduced lung cancer combined with emphysema. Apart from micro-CT, other techniques have also been used to complement the data. Results show the use of micro-CT and automated image analysis to quantify the effect of different pulmonary diseases on small animal models. Methods presented in this thesis will be of use to characterize other models of lung disease, as well as for treatment testing.