In depth understanding of mouse lung conducting airway structure is lacking. Building on this knowledge will aid in designing critical studies that establish relationship between structure and function and its alteration in small-animal based disease models. For example, being able to track the effect of particle inhalation from the nose to the nasal passages to the lung is a highly
warranted tool in various research areas of pulmonology.
Here, we present results from a crude proof-of-concept algorithm that performs a semi-automatic segmentation of the lung airways. Using a combination of user defined seed points, local neighborhood based statistics region growing techniques and tree topology based search algorithm, a reasonable estimate of airway morphology may be easily obtained (publication currently being written) without extensive pre-processing. From these models metrics such as pathway length, diameter, area etc. may be easily calculated.
Here, the lung was scanned using Scanco50, specimen Micro-CT scanner and analyzed using in-house Tcl scripts on Amira platform. From scanning to volume quantification, the process takes about 4hours for the whole lung.
Bino Varghese PhD, Imaging Specialist- Advanced Preclinical Imaging @ Molecular Imaging Center