Name
Abstract | ||
|---|---|---|
Producing manual, pixel-accurate, image segmentation labels is tedious and time-consuming. This is often a rate-limiting factor when large amounts of labeled images are required, such as for training deep convolutional networks for instrument-background segmentation in surgical scenes. No large datasets comparable to industry standards in the computer vision community are available for this task. ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TMI.2021.3057884 | IEEE Transactions on Medical Imaging |
Keywords | DocType | Volume |
Image segmentation,Instruments,Tools,Training,Task analysis,Surgery,Manuals | Journal | 40 |
Issue | ISSN | Citations |
5 | 0278-0062 | 2 |
PageRank | References | Authors |
0.36 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luis C. Garcia-Peraza-Herrera | 1 | 19 | 4.59 |
| Lucas Fidon | 2 | 45 | 4.14 |
| Claudia DrEttorre | 3 | 2 | 0.36 |
| Danail Stoyanov | 4 | 792 | 81.36 |
| Tom Vercauteren | 5 | 1956 | 108.68 |
| Sébastien Ourselin | 6 | 576 | 57.16 |