Name
Title | ||
|---|---|---|
Segmentation of branching vascular structures using adaptive subdivision surface fitting |
Abstract | ||
|---|---|---|
This paper describes a novel method for segmentation and modeling of branching vessel structures in medical images using adaptive subdivision surfaces fitting. The method starts with a rough initial skeleton model of the vessel structure. A coarse triangular control mesh consisting of hexagonal rings and dedicated bifurcation elements is constructed from this skeleton. Special attention is paid to ensure a topological sound control mesh is created around the bifurcation areas. Then, a smooth tubular surface is obtained from this coarse mesh using a standard subdivision scheme. This subdivision surface is iteratively fitted to the image. During the fitting, the target update locations of the subdivision surface are obtained using a scanline search along the surface normals, finding the maximum gradient magnitude (of the imaging data). In addition to this surface fitting framework, we propose an adaptive mesh refinement scheme. In this step the coarse control mesh topology is updated based on the current segmentation result, enabling adaptation to varying vessel lumen diameters. This enhances the robustness and flexibility of the method and reduces the amount of prior knowledge needed to create the initial skeletal model. The method was applied to publicly available CTA data from the Carotid Bifurcation Algorithm Evaluation Frameworkl resulting in an average dice index of 89.2% with the ground truth. Application of the method to the complex vascular structure of a coronary artery tree in CTA and to MRI images were performed to show the versatility and flexibility of the proposed framework. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1117/12.2082222 | Proceedings of SPIE |
Keywords | Field | DocType |
subdivision,vessel,segmentation,modeling | Computer vision,Mesh networking,Segmentation,Adaptive mesh refinement,Image segmentation,Robustness (computer science),Subdivision surface,Subdivision,Artificial intelligence,Scan line,Physics | Conference |
Volume | ISSN | Citations |
9413 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pieter H Kitslaar | 1 | 120 | 7.95 |
| ronald van t klooster | 2 | 7 | 1.22 |
| Marius Staring | 3 | 971 | 59.25 |
| B.P.F. Lelieveldt | 4 | 1331 | 115.59 |
| Rob J. Van Der Geest | 5 | 559 | 56.91 |