Name
Abstract | ||
|---|---|---|
The pigtail catheter is a type of catheter inserted into the human body during interventional surgeries such as the transcatheter aortic valve implantation (TAVI). The catheter is characterized by a tightly curled end in order to remain attached to a valve pocket during the intervention, and it is used to inject contrast agent for the visualization of the vessel in fluoroscopy. Image-based detection of this catheter is used during TAVI, in order to overlay a model of the aorta and enhance visibility during the surgery. Due to the different possible projection angles in fluoroscopy, the pigtail tip can appear in a variety of different shapes spanning from pure circular to ellipsoid or even line. Furthermore, the appearance of the catheter tip is radically altered when the contrast agent is injected during the intervention or when it is occluded by other devices. All these factors make the robust real-time detection and tracking of the pigtail catheter a challenging task. To address these challenges, this paper proposes a new tree-structured, hierarchical detection scheme, based on a shape categorization of the pigtail catheter tip, and a combination of novel Haar features. The proposed framework demonstrates improved detection performance, through a validation on a data set consisting of 272 sequences with more than 20,000 images. The detection framework presented in this paper is not limited to pigtail catheter detection, but it can also be applied successfully to any other shape-varying object with similar characteristics. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1117/12.911084 | Proceedings of SPIE |
Keywords | Field | DocType |
Pigtail catheter,catheter detection,TAVI,shape-varying object detection | Computer vision,Catheter,Pigtail,Haar-like features,Aortic valve,Fluoroscopy,Artificial intelligence,Physics | Conference |
Volume | ISSN | Citations |
8316 | 0277-786X | 1 |
PageRank | References | Authors |
0.37 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stratis Tzoumas | 1 | 19 | 4.86 |
| Peng Wang | 2 | 77 | 8.30 |
| Yefeng Zheng | 3 | 1391 | 114.67 |
| Matthias John | 4 | 119 | 11.36 |
| Dorin Comaniciu | 5 | 8389 | 601.83 |