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