Lung Extraction, Lobe Segmentation and Hierarchical Region Assessment for Quantitative Analysis on High Resolution Computed Tomography Images

Regional assessment of lung disease (such as chronic obstructive pulmonary disease) is a critical component to accurate patient diagnosis. Software tools than enable such analysis are also important for clinical research studies. In this work, we present an image segmentation and data representation framework that enables quantitative analysis specific to different lung regions on high resolution computed tomography (HRCT) datasets. We present an offline, fully automatic image processing chain that generates airway, vessel, and lung mask segmentations in which the left and right lung are delineated. We describe a novel lung lobe segmentation tool that produces reproducible results with minimal user interaction. A usability study performed across twenty datasets (inspiratory and expiratory exams including a range of disease states) demonstrates the tool's ability to generate results within five to seven minutes on average. We also describe a data representation scheme that involves compact encoding of label maps such that both "regions" (such as lung lobes) and "types" (Such as emphysematous parenchyma) can be simultaneously represented at a given location in the HRCT.