title: Automated generation of directed graphs from vascular segmentations.
abstract: Automated feature extraction from medical images is an important task in imaging informatics. We describe a graph-based technique for automatically identifying vascular substructures within a vascular tree segmentation. We illustrate our technique using vascular segmentations from computed tomography pulmonary angiography images. The segmentations were acquired in a semi-automated fashion using existing segmentation tools. A 3D parallel thinning algorithm was used to generate the vascular skeleton and then graph-based techniques were used to transform the skeleton to a directed graph with bifurcations and endpoints as nodes in the graph. Machine-learning classifiers were used to automatically prune false vascular structures from the directed graph. Semantic labeling of portions of the graph with pulmonary anatomy (pulmonary trunk and left and right pulmonary arteries) was achieved with high accuracy (percent correct⩾0.97). Least-squares cubic splines of the centerline paths between nodes were computed and were used to extract morphological features of the vascular tree. The graphs were used to automatically obtain diameter measurements that had high correlation (r⩾0.77) with manual measurements made from the same arteries.
authors.affiliation: University of Utah, Department of Radiology, 729 Arapeen Drive, Salt Lake City, UT 84108, United States. Electronic address: brian.chapman@utah.edu.
authors.forename: Brian E
authors.initials: BE
authors.lastname: Chapman
authors.affiliation: University of Pittsburgh, Department of Biomedical Informatics, 5607 Baum Boulevard BAUM 423, Pittsburgh, PA 15206-3701, United States.
authors.forename: Holly P
authors.initials: HP
authors.lastname: Berty
authors.affiliation: University of Utah, Department of Mathematics, 155 S 1400 E, Salt Lake City, UT 84112-0090, United States.
authors.forename: Stuart L
authors.initials: SL
authors.lastname: Schulthies
mesh_terms: D000465:Algorithms; D000792:Angiography; D001011:Aorta / Q000473:pathology; D019540:Area Under Curve; D001331:Automation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D016018:Least-Squares Analysis; D000069550:Machine Learning; D011651:Pulmonary Artery / Q000000981:diagnostic imaging* / Q000473:pathology* / Q000502:physiology; D012372:ROC Curve; D012984:Software; D014057:Tomography, X-Ray Computed
keywords: Feature recognition; Medical imaging; Segmentation