SurGen: Text-Guided Diffusion Model for Surgical Video Generation

• URL: http://arxiv.org/abs/2408.14028v3
• Date: Tue, 24 Sep 2024 23:23:50 GMT
• Title: SurGen: Text-Guided Diffusion Model for Surgical Video Generation
• Authors: Joseph Cho, Samuel Schmidgall, Cyril Zakka, Mrudang Mathur, Dhamanpreet Kaur, Rohan Shad, William Hiesinger,
• Abstract summary: SurGen is a text-guided diffusion model tailored for surgical video synthesis. We validate the visual and temporal quality of the outputs using standard image and video generation metrics. Our results demonstrate the potential of diffusion models to serve as valuable educational tools for surgical trainees.
• Score: 0.6551407780976953
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Diffusion-based video generation models have made significant strides, producing outputs with improved visual fidelity, temporal coherence, and user control. These advancements hold great promise for improving surgical education by enabling more realistic, diverse, and interactive simulation environments. In this study, we introduce SurGen, a text-guided diffusion model tailored for surgical video synthesis. SurGen produces videos with the highest resolution and longest duration among existing surgical video generation models. We validate the visual and temporal quality of the outputs using standard image and video generation metrics. Additionally, we assess their alignment to the corresponding text prompts through a deep learning classifier trained on surgical data. Our results demonstrate the potential of diffusion models to serve as valuable educational tools for surgical trainees.

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