Effects of Path-Finding Algorithms on the Labeling of the Centerlines of Circle of Willis Arteries

Kim, Se-On; Kim, Yoon-Chul

doi:10.3390/tomography9040113

Cited by 5 publications

(6 citation statements)

References 37 publications

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“…In this study, we focused on the ICA’s C4–C7 segment, which typically contains highly tortuous arterial geometry. After the skeletonization process, the segmented ICA can produce spurious centerlines, which result in erroneous paths in certain cases when using the shortest path-finding algorithms [ 17 ].…”

Section: Discussionmentioning

confidence: 99%

“…Sixty subjects’ data were considered to evaluate the performance of path-finding algorithms in the ICA segments. Quite a few subjects’ data had the problem of incorrect path findings in the ICAs when the Dijkstra and A* algorithms were used [ 17 ]. This motivated us to develop a new method in order to improve the accuracy of the path-finding procedure in the ICA segments.…”

Section: Methodsmentioning

confidence: 99%

“…Path-finding algorithms have been used to automatically extract the centerlines of arteries [ 16 , 17 , 18 , 19 ]. The Dijkstra and A* algorithms are well known for their effectiveness in finding the shortest path between two endpoints of an artery’s centerline [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

“…The Dijkstra and A* algorithms are well known for their effectiveness in finding the shortest path between two endpoints of an artery’s centerline [ 16 ]. A recent experimental study has indicated that these two algorithms are accurate in most arterial segments in the circle of Willis, except for the internal carotid arteries (ICAs) [ 17 ]. ICAs are inherently tortuous at the level of C4–C7 [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Improved Path-Finding Method for the Tracking of Centerlines of Tortuous Internal Carotid Arteries in MR Angiography

Kim,

Kim

2024

J. Imaging

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Centerline tracking is useful in performing segmental analysis of vessel tortuosity in angiography data. However, a highly tortuous) artery can produce multiple centerlines due to over-segmentation of the artery, resulting in inaccurate path-finding results when using the shortest path-finding algorithm. In this study, the internal carotid arteries (ICAs) from three-dimensional (3D) time-of-flight magnetic resonance angiography (TOF MRA) data were used to demonstrate the effectiveness of a new path-finding method. The method is based on a series of depth-first searches (DFSs) with randomly different orders of neighborhood searches and produces an appropriate path connecting the two endpoints in the ICAs. It was compared with three existing methods which were (a) DFS with a sequential order of neighborhood search, (b) Dijkstra algorithm, and (c) A* algorithm. The path-finding accuracy was evaluated by counting the number of successful paths. The method resulted in an accuracy of 95.8%, outperforming the three existing methods. In conclusion, the proposed method has been shown to be more suitable as a path-finding procedure than the existing methods, particularly in cases where there is more than one centerline resulting from over-segmentation of a highly tortuous artery.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Improved Path-Finding Method for the Tracking of Centerlines of Tortuous Internal Carotid Arteries in MR Angiography

Kim,

Kim

2024

J. Imaging

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“…The morphology.skeletonize function of the Scikit-Image library [24] was used to obtain the 3D centerlines of the vessels. The Plotly (Plotly Technologies Inc., Montreal, QC, Canada) Python library (https://plotly.com/python (accessed on 25 October 2023)) was used to manually annotate the landmarks in the circle of Willis arteries [25]. The Dijkstra algorithm was used to find a path between two manually annotated endpoints.…”

Section: Data and Preprocessingmentioning

confidence: 99%

Assessing Machine Learning Models for Predicting Age with Intracranial Vessel Tortuosity and Thickness Information

Yoon,

Oh,

Kim

2023

Brain Sciences

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This study aimed to develop and validate machine learning (ML) models that predict age using intracranial vessels’ tortuosity and diameter features derived from magnetic resonance angiography (MRA) data. A total of 171 subjects’ three-dimensional (3D) time-of-flight MRA image data were considered for analysis. After annotations of two endpoints in each arterial segment, tortuosity features such as the sum of the angle metrics, triangular index, relative length, and product of the angle distance, as well as the vessels’ diameter features, were extracted and used to train and validate the ML models for age prediction. Features extracted from the right and left internal carotid arteries (ICA) and basilar arteries were considered as the inputs to train and validate six ML regression models with a four-fold cross validation. The random forest regression model resulted in the lowest root mean square error of 14.9 years and the highest average coefficient of determination of 0.186. The linear regression model showed the lowest average mean absolute percentage error (MAPE) and the highest average Pearson correlation coefficient (0.532). The mean diameter of the right ICA vessel segment was the most important feature contributing to prediction of age in two out of the four regression models considered. An ML of tortuosity descriptors and diameter features extracted from MRA data showed a modest correlation between real age and ML-predicted age. Further studies are warranted for the assessment of the model’s age predictions in patients with intracranial vessel diseases.

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Deep-learning-based extraction of circle of Willis topology with anatomical priors

Alblas,

Vos,

Lipplaa

et al. 2024

Sci Rep

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The circle of Willis (CoW) is a circular arrangement of arteries in the human brain, exhibiting significant anatomical variability. The CoW is extensively studied in relation to neurovascular pathologies, with certain anatomical variants previously linked to ischemic stroke and intracranial aneurysms. In an individual CoW, arteries might be absent (aplasia) or underdeveloped (hypoplasia, diameter < 1 mm). As the assessment of such variations is time-consuming and susceptible to subjectivity, robust automatic extraction of personalized CoW topology from time-of-flight magnetic resonance angiography (TOF-MRA) images would highly benefit large-scale clinical investigations. Previous work has sought to extract CoW topology from voxel-based semantic segmentation masks. However, hypoplastic arteries are challenging to recover in voxel-based segmentation. Instead, we propose using a complete CoW as an anatomical prior for extracting all possible CoW arteries as shortest paths between automatically identified anatomical landmarks, guided by automatically determined artery orientation vector fields. These fields are obtained using a scale-invariant and rotation-equivariant mesh-CNN-based model (SIRE). For a 3D TOF-MRA volume, a potentially overcomplete graph of the CoW is thus extracted in which each edge represents an artery. Subsequently, a binary Random Forest classifier labels each artery as normal or hypo-/aplastic. The model was optimized and validated using a data set of 351 3D TOF-MRA scans in a cross-validation setup. We showed that using a shortest path algorithm with a cost function based on local artery orientations results in continuous artery paths, even in hypoplastic cases. We tracked the correct path in the posterior communicating arteries in 70–74% of the cases, an artery that is known to pose challenges in voxel-based segmentation models. Our downstream artery path classifier obtained an average F1 score of 0.91, demonstrating the potential of our proposed framework to extract personalized CoW topology automatically.

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Effects of Path-Finding Algorithms on the Labeling of the Centerlines of Circle of Willis Arteries

Cited by 5 publications

References 37 publications

An Improved Path-Finding Method for the Tracking of Centerlines of Tortuous Internal Carotid Arteries in MR Angiography

An Improved Path-Finding Method for the Tracking of Centerlines of Tortuous Internal Carotid Arteries in MR Angiography

Assessing Machine Learning Models for Predicting Age with Intracranial Vessel Tortuosity and Thickness Information

Deep-learning-based extraction of circle of Willis topology with anatomical priors

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