Automated Quantitative Analysis of a Mouse Model of Chronic Pulmonary Inflammation using Micro X-ray Computed Tomography

Micro-CT has emerged as an excellent tool for in-vivo imaging of the lungs of small laboratory animals. Several studies have shown that it can be used to assess the evolution of pulmonary lung diseases in longitudinal studies. However, most of them rely on non-automatic tools for image analysis, or are merely qualitative. In this article, we present a longitudinal, quantitative study of a mouse model of silica-induced pulmonary inflammation. To automatically assess disease progression, we have devised and validated a lung segmentation method that combines threshold-based segmentation, atlas-based segmentation and level sets. Our volume measurements, based on the automatic segmentations, point at a compensation mechanism which leads to an increase of the healthy lung volume in response to the loss of functional tissue caused by inflammation

Growth Pattern Analysis of Murine Lung Neoplasms by Advanced Semi-Automated Quantification of Micro-CT Images

Computed tomography (CT) is a non-invasive imaging modality used to monitor human lung cancers. Typically, tumor volumes are calculated using manual or semi-automated methods that require substantial user input, and an exponential growth model is used to predict tumor growth. However, these measurement methodologies are time-consuming and can lack consistency. In addition, the availability of datasets with sequential images of the same tumor that are needed to characterize in vivo growth patterns for human lung cancers is limited due to treatment interventions and radiation exposure associated with multiple scans. In this paper, we performed micro-CT imaging of mouse lung cancers induced by overexpression of ribonucleotide reductase, a key enzyme in nucleotide biosynthesis, and developed an advanced semi-automated algorithm for efficient and accurate tumor volume measurement. Tumor volumes determined by the algorithm were first validated by comparison with results from manual methods for volume determination as well as direct physical measurements. A longitudinal study was then performed to investigate in vivo murine lung tumor growth patterns. Individual mice were imaged at least three times, with at least three weeks between scans. The tumors analyzed exhibited an exponential growth pattern, with an average doubling time of 57.08 days. The accuracy of the algorithm in the longitudinal study was also confirmed by comparing its output with manual measurements. These results suggest an exponential growth model for lung neoplasms and establish a new advanced semi-automated algorithm to measure lung tumor volume in mice that can aid efforts to improve lung cancer diagnosis and the evaluation of therapeutic responses

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

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

Automatic population HARDI white matter tract clustering by label fusion of multiple tract atlases

Automatic labeling of white matter fibres in diffusion-weighted brain MRI is vital for comparing brain integrity and connectivity across populations, but is challenging. Whole brain tractography generates a vast set of fibres throughout the brain, but it is hard to cluster them into anatomically meaningful tracts, due to wide individual variations in the trajectory and shape of white matter pathways. We propose a novel automatic tract labeling algorithm that fuses information from tractography and multiple hand-labeled fibre tract atlases. As streamline tractography can generate a large number of false positive fibres, we developed a top-down approach to extract tracts consistent with known anatomy, based on a distance metric to multiple hand-labeled atlases. Clustering results from different atlases were fused, using a multi-stage fusion scheme. Our “label fusion” method reliably extracted the major tracts from 105-gradient HARDI scans of 100 young normal adults