Intuitive Surgical SurgToolLoc and SurgVU Challenges Results: 2022-2025
Aneeq Zia, Max Berniker, Rogerio Garcia Nespolo, Kiran Bhattacharyya, Xi Liu, Ziheng Wang, Satoshi Kondo, Satoshi Kasai, Kousuke Hirasawa, Bo Liu, David Austin, Yiheng Wang, Michal Futrega, Jean‐François Puget, Zhenqiang Li, Yoichi Sato, Ryo Fujii, Ryo Hachiuma, Mana Masuda, Hideo Saitô
- Year
- 2023
- Citations
- 6
- Access
- Open access
Abstract
Robotic assisted (RA) surgery promises to transform surgical intervention. Intuitive Surgical is committed to fostering these changes and the machine learning models and algorithms that will enable them. With these goals in mind we have invited the surgical data science community to participate in a yearly competition hosted through the Medical Imaging Computing and Computer Assisted Interventions (MICCAI) conference. With varying changes from year to year, we have challenged the community to solve difficult machine learning problems in the context of advanced RA applications. Here we document the results of these challenges, focusing on surgical tool localization (SurgToolLoc) and surgical visual understanding (SurgVU). The publicly released dataset that accompanies these challenges is detailed in a separate paper arXiv:2501.09209 [1].
Keywords
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