Name
Abstract | ||
|---|---|---|
One of the most complex and difficult tasks for surgeons during minimally invasive interventions is suturing. A prerequisite to assist the suturing process is the tracking of the needle. The endoscopic images provide a rich source of information which can be used for needle tracking. In this paper, we present an image-based method for markerless needle tracking. The method uses a color-based and geometry-based segmentation to detect the needle. Once an initial needle detection is obtained, a region of interest enclosing the extracted needle contour is passed on to a reduced segmentation It is evaluated with in vivo images from da Vinci interventions. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1117/12.2081920 | Proceedings of SPIE |
Keywords | Field | DocType |
Surgical Vision,Localization & Tracking Technologies,Endoscopic Image Processing,Robotic-assisted surgery,Laparoscopic procedures | Computer vision,Segmentation,Suturing needle,Image based,Image segmentation,Artificial intelligence,Region of interest,Robotic assisted surgery,Physics | Conference |
Volume | ISSN | Citations |
9415 | 0277-786X | 1 |
PageRank | References | Authors |
0.38 | 1 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefanie Speidel | 1 | 313 | 39.70 |
| a kroehnert | 2 | 1 | 0.38 |
| Sebastian Bodenstedt | 3 | 91 | 16.46 |
| Hannes Kenngott | 4 | 104 | 22.28 |
| Beat P. Müller-Stich | 5 | 79 | 12.09 |
| Rüdiger Dillmann | 6 | 433 | 43.19 |