Accelerating Surgical Skill Acquisition by Using Multi-View Bullet-Time Video Generation
Surgical education and training have seen significant advancements with the integration of innovative technologies. This paper presents a novel approach to surgical education using a multi-view capturing system and bullet-time generation techniques to enhance the learning experience for aspiring surgeons. The proposed system leverages an array of synchronized cameras strategically positioned around a surgical simulation environment, enabling the capture of surgical procedures from multiple angles simultaneously. The captured multi-view data is then processed using advanced computer vision and image processing algorithms to create a “bullet-time” effect, similar to the iconic scenes from The Matrix movie, allowing educators and trainees to manipulate time and view the surgical procedure from any desired perspective. In this paper, we propose the technical aspects of the multi-view capturing system, the bullet-time generation process, and the integration of these technologies into surgical education programs. We also discuss the potential applications in various surgical specialties and the benefits of utilizing this system for both novice and experienced surgeons. Finally, we present preliminary results from pilot studies and user feedback, highlighting the promising potential of this innovative approach to revolutionize surgical education and training.
Authors
- Koichiro Kumano (ORCID: https://orcid.org/0000-0001-8032-167X)
- Itaru Kitahara (ORCID: https://orcid.org/0000-0002-5186-789X)
- Ying-Hao Wang
- Daichi Kitaguchi (ORCID: https://orcid.org/0000-0002-8197-6317)
- Tatsuya Oda (ORCID: https://orcid.org/0000-0001-6115-0158)
- Chun Xie (ORCID: https://orcid.org/0000-0003-4936-7404)
- Shinji Hashimoto (ORCID: https://orcid.org/0000-0002-9346-3228)
Institutions
- University of Tsukuba (JP)
Publication Details
- Journal
- Applied Sciences
- Published
- 2025-08-10
- DOI
- https://doi.org/10.3390/app15168830
- Primary Topic
- Surgical Simulation and Training
- Type
- article
- Field-Weighted Citation Impact
- 0.00