Name
Title | ||
|---|---|---|
Fast catheter segmentation from echocardiographic sequences based on segmentation from corresponding X-ray fluoroscopy for cardiac catheterization interventions. |
Abstract | ||
|---|---|---|
Echocardiography is a potential alternative to X-ray fluoroscopy in cardiac catheterization given its richness in soft tissue information and its lack of ionizing radiation. However, its small field of view and acoustic artifacts make direct automatic segmentation of the catheters very challenging. In this study, a fast catheter segmentation framework for echocardiographic imaging guided by the segmentation of corresponding X-ray fluoroscopic imaging is proposed. The complete framework consists of: 1) catheter initialization in the first X-ray frame; 2) catheter tracking in the rest of the X-ray sequence; 3) fast registration of corresponding X-ray and ultrasound frames; and 4) catheter segmentation in ultrasound images guided by the results of both X-ray tracking and fast registration. The main contributions include: 1) a Kalman filter-based growing strategy with more clinical data evalution; 2) a SURF detector applied in a constrained search space for catheter segmentation in ultrasound images; 3) a two layer hierarchical graph model to integrate and smooth catheter fragments into a complete catheter; and 4) the integration of these components into a system for clinical applications. This framework is evaluated on five sequences of porcine data and four sequences of patient data comprising more than 3000 X-ray frames and more than 1000 ultrasound frames. The results show that our algorithm is able to track the catheter in ultrasound images at 1.3 s per frame, with an error of less than 2 mm. However, although this may satisfy the accuracy for visualization purposes and is also fast, the algorithm still needs to be further accelerated for real-time clinical applications. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TMI.2014.2360988 | IEEE Trans. Med. Imaging |
Keywords | Field | DocType |
x-ray tracking,clinical data evaluation,diagnostic radiography,real-time clinical applications,cardiac catheterization,fast catheter segmentation framework,complete catheter,kalman filters,x-ray frames,soft tissue information,visualization purposes,catheter fragments,ionizing radiation,image segmentation,segmentation,hierarchical graph model,porcine data,echocardiographic imaging,ultrasound images,catheter tracking,echocardiographic sequences,x-ray fluoroscopic imaging segmentation,fast registration,acoustic artifacts,x-ray sequence,small field of view,constrained search space,direct automatic segmentation,complete framework,surf detector,cardiac catheterization intervention,catheter initialization,tracking,image sequences,first x-ray frame,graph theory,kalman filter-based growing strategy,image registration,biological tissues,patient data,x-ray fluoroscopy,medical image processing,echocardiography,ultrasound frames,fluoroscopy,catheters,detectors,feature extraction | Computer vision,Catheter,Segmentation,Visualization,Image segmentation,Fluoroscopy,Feature extraction,Artificial intelligence,Cardiac catheterization,Mathematics,Ultrasound | Journal |
Volume | Issue | ISSN |
34 | 4 | 1558-254X |
Citations | PageRank | References |
7 | 0.44 | 21 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xianliang Wu | 1 | 43 | 8.87 |
| R. James Housden | 2 | 50 | 9.83 |
| YingLiang Ma | 3 | 251 | 26.76 |
| Benjamin Razavi | 4 | 7 | 0.44 |
| Kawal S. Rhode | 5 | 759 | 78.72 |
| Daniel Rueckert | 6 | 9338 | 637.58 |