Vessel Geometry Estimation for Patients with Peripheral Artery Disease

• Published in: Sensors (Basel, Switzerland) Switzerland), 2024-10, Vol.24 (19), p.6441
• Main Authors: Saeed, Hassan, Skalski, Andrzej
• Format: Article
• Language: English
• Subjects: 3D imaging; Accuracy; Algorithms; Automation; Cardiovascular disease; Care and treatment; centerline; Coronary vessels; Deep learning; Diseases; distance transform; geometry measurements; Humans; Imaging, Three-Dimensional - methods; Older people; Peripheral Arterial Disease - diagnostic imaging; Peripheral Arterial Disease - pathology; Peripheral Arterial Disease - physiopathology; peripheral arteries; Surgeons; Vein & artery diseases
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24196441

The estimation of vessels' centerlines is a critical step in assessing the geometry of the vessel, the topological representation of the vessel tree, and vascular network visualization. In this research, we present a novel method for obtaining geometric parameters from peripheral arteries in 3D medical binary volumes. Our approach focuses on centerline extraction, which yields smooth and robust results. The procedure starts with a segmented 3D binary volume, from which a distance map is generated using the Euclidean distance transform. Subsequently, a skeleton is extracted, and seed points and endpoints are identified. A search methodology is used to derive the best path on the skeletonized 3D binary array while tracking from the goal points to the seed point. We use the distance transform to calculate the distance between voxels and the nearest vessel surface, while also addressing bifurcations when vessels divide into multiple branches. The proposed method was evaluated on 22 real cases and 10 synthetically generated vessels. We compared our method to different state-of-the-art approaches and demonstrated its better performance. The proposed method achieved an average error of 1.382 mm with real patient data and 0.571 mm with synthetic data, both of which are lower than the errors obtained by other state-of-the-art methodologies. This extraction of the centerline facilitates the estimation of multiple geometric parameters of vessels, including radius, curvature, and length.