首页|Automated generation of directed graphs from vascular segmentations
Automated generation of directed graphs from vascular segmentations
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NSTL
Elsevier
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. (C) 2015 Elsevier Inc. All rights reserved.
Medical imagingSegmentationFeature recognition
Chapman, Brian E.、Berty, Holly P.、Schulthies, Stuart L.
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Univ Utah, Dept Radiol, Salt Lake City, UT 84108 USA
Univ Pittsburgh, Dept Biomed Informat, Pittsburgh, PA 15206 USA
Univ Utah, Dept Math, Salt Lake City, UT 84112 USA
NSTL
Elsevier
