Name
Abstract | ||
|---|---|---|
We present a self-supervised approach to training convolutional neural networks for dense depth estimation from monocular endoscopy data without a priori modeling of anatomy or shading. Our method only requires sequential data from monocular endoscopic videos and a multi-view stereo reconstruction method, e.g. structure from motion, that supervises learning in a sparse but accurate manner. Consequently, our method requires neither manual interaction, such as scaling or labeling, nor patient CT in the training and application phases. We demonstrate the performance of our method on sinus endoscopy data from two patients and validate depth prediction quantitatively using corresponding patient CT scans where we found submillimeter residual errors. (Link to the supplementary video: https://camp.lcsr.jhu.edu/miccai-2018-demonstration-videos/) |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-01201-4_15 | OR 2.0 CONTEXT-AWARE OPERATING THEATERS, COMPUTER ASSISTED ROBOTIC ENDOSCOPY, CLINICAL IMAGE-BASED PROCEDURES, AND SKIN IMAGE ANALYSIS, OR 2.0 2018 |
DocType | Volume | ISSN |
Conference | 11041 | 0302-9743 |
Citations | PageRank | References |
1 | 0.37 | 19 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xingtong Liu | 1 | 13 | 5.02 |
| Ayushi Sinha | 2 | 24 | 6.72 |
| mathias unberath | 3 | 56 | 24.46 |
| Masaru Ishii | 4 | 141 | 16.84 |
| Hager Gregory D | 5 | 1946 | 159.37 |
| Russell H. Taylor | 6 | 1970 | 438.00 |
| Austin Reiter | 7 | 164 | 13.02 |