ArticleTomography (Ann Arbor, Mich.)2023
Effects of Path-Finding Algorithms on the Labeling of the Centerlines of Circle of Willis Arteries.
Article in Tomography (Ann Arbor, Mich.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Precision in Complexity: A Protocol-Driven Quantitative Anatomic Strategy for Giant Olfactory Groove Meningioma Resection in a High-Risk Geriatric Patient.Diagnostics (Basel, Switzerland) · 2026Article
- Machine Learning-Based Age Prediction with Feature Subset Selection from Magnetic Resonance Angiography Data.Healthcare informatics research · 2025Article
- Deep-learning-based extraction of circle of Willis topology with anatomical priors.Scientific reports · 2024Article
- Vessel Geometry Estimation for Patients with Peripheral Artery Disease.Sensors (Basel, Switzerland) · 2024Article
- Clinical Applications of Artificial Intelligence in Medical Imaging and Image Processing-A Review.Cancers · 2024Article
- An Improved Path-Finding Method for the Tracking of Centerlines of Tortuous Internal Carotid Arteries in MR Angiography.Journal of imaging · 2024Article
- Assessing Machine Learning Models for Predicting Age with Intracranial Vessel Tortuosity and Thickness Information.Brain sciences · 2023Article
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2 authors.
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Abstract
Quantitative analysis of intracranial vessel segments typically requires the identification of the vessels' centerlines, and a path-finding algorithm can be used to automatically detect vessel segments' centerlines. This study compared the performance of path-finding algorithms for vessel labeling. Three-dimensional (3D) time-of-flight magnetic resonance angiography (MRA) images from the publicly available dataset were considered for this study. After manual annotations of the endpoints of each vessel segment, three path-finding methods were compared: (Method 1) depth-first search algorithm, (Method 2) Dijkstra's algorithm, and (Method 3) A* algorithm. The rate of correctly found paths was quantified and compared among the three methods in each segment of the circle of Willis arteries. In the analysis of 840 vessel segments, Method 2 showed the highest accuracy (97.1%) of correctly found paths, while Method 1 and 3 showed an accuracy of 83.5% and 96.1%, respectively. The AComm artery was highly inaccurately identified in Method 1, with an accuracy of 43.2%. Incorrect paths by Method 2 were noted in the R-ICA, L-ICA, and R-PCA-P1 segments. The Dijkstra and A* algorithms showed similar accuracy in path-finding, and they were comparable in the speed of path-finding in the circle of Willis arterial segments.
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