Summary of Egosurgery-tool: a Dataset Of Surgical Tool and Hand Detection From Egocentric Open Surgery Videos, by Ryo Fujii and Hideo Saito and Hiroki Kajita
EgoSurgery-Tool: A Dataset of Surgical Tool and Hand Detection from Egocentric Open Surgery Videos
by Ryo Fujii, Hideo Saito, Hiroki Kajita
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces EgoSurgery-Tool, a large-scale dataset for surgical tool detection in egocentric open surgery videos. The dataset contains real video recordings captured using an egocentric camera attached to the surgeon’s head, along with phase and annotation information. EgoSurgery-Tool comprises over 49K bounding boxes across 15 categories, making it superior to existing datasets due to its larger scale, greater variety of surgical tools, more annotations, and denser scenes. The paper also evaluates the effectiveness of nine popular object detectors in both surgical tool and hand detection using a comprehensive analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a big dataset with lots of videos of surgeries from different angles. It’s hard to find these kinds of videos because they’re very rare and usually not shared publicly. This dataset will help people develop better machines that can recognize what tools the surgeon is using during surgery, which is important for understanding what’s happening in the operating room. |
