Summary of Sar-rarp50: Segmentation Of Surgical Instrumentation and Action Recognition on Robot-assisted Radical Prostatectomy Challenge, by Dimitrios Psychogyios et al.
SAR-RARP50: Segmentation of surgical instrumentation and Action Recognition on Robot-Assisted Radical Prostatectomy Challenge
by Dimitrios Psychogyios, Emanuele Colleoni, Beatrice Van Amsterdam, Chih-Yang Li, Shu-Yu Huang, Yuchong Li, Fucang Jia, Baosheng Zou, Guotai Wang, Yang Liu, Maxence Boels, Jiayu Huo, Rachel Sparks, Prokar Dasgupta, Alejandro Granados, Sebastien Ourselin, Mengya Xu, An Wang, Yanan Wu, Long Bai, Hongliang Ren, Atsushi Yamada, Yuriko Harai, Yuto Ishikawa, Kazuyuki Hayashi, Jente Simoens, Pieter DeBacker, Francesco Cisternino, Gabriele Furnari, Alex Mottrie, Federica Ferraguti, Satoshi Kondo, Satoshi Kasai, Kousuke Hirasawa, Soohee Kim, Seung Hyun Lee, Kyu Eun Lee, Hyoun-Joong Kong, Kui Fu, Chao Li, Shan An, Stefanie Krell, Sebastian Bodenstedt, Nicolas Ayobi, Alejandra Perez, Santiago Rodriguez, Juanita Puentes, Pablo Arbelaez, Omid Mohareri, Danail Stoyanov
First submitted to arxiv on: 31 Dec 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| 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 EndoVis 2022 SAR-RARP50 challenge is a dataset for surgical action recognition and semantic instrumentation segmentation in robotic-assisted radical prostatectomy (RARP) procedures. The goal is to develop robust single-task approaches or multitask-based methods that integrate both tasks. The dataset contains 50 suturing video segments, offering an opportunity for researchers to explore the potential of learning-based approaches in the surgical domain. Twelve teams participated in the challenge, contributing action recognition methods, instrument segmentation techniques, and multitask approaches that combined both tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to recognize different actions in a surgery video, like tying knots or cutting tissue. This is important because it can help robots work better alongside surgeons. To make this happen, we need good datasets with lots of examples. That’s where the EndoVis 2022 SAR-RARP50 challenge comes in. It gives researchers a big dataset to play with, so they can develop new methods for recognizing actions and separating tools from each other. The goal is to make these approaches more accurate and efficient. |
