bibtext: @InProceedings{ Li_Endora_MICCAI2024,
                   author = { Li, Chenxin and Liu, Hengyu and Liu, Yifan and Feng, Brandon Y. and Li, Wuyang and Liu, Xinyu and Chen, Zhen and Shao, Jing and Yuan, Yixuan },
                   title = { { Endora: Video Generation Models as Endoscopy Simulators } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15006 },
                   month = {October},
                   pages = { pending },
                }
abstract: Generative models hold promise for revolutionizing medical education, robot-assisted surgery, and data augmentation for machine learning.  Despite progress in generating 2D medical images, the complex domain of clinical video generation has largely remained untapped. This paper introduces Endora, an innovative approach to generate medical videos to simulate clinical endoscopy scenes. We present a novel generative model design that integrates a meticulously crafted video transformer with advanced 2D vision foundation model priors, explicitly modeling spatial-temporal dynamics during video generation. We also pioneer the first public benchmark for endoscopy simulation with video generation models, adapting existing state-of-the-art methods for this endeavor. Endora demonstrates exceptional visual quality in generating endoscopy videos, surpassing state-of-the-art methods in extensive testing. Moreover, we explore how this endoscopy simulator can empower downstream video analysis tasks and even generate 3D medical scenes with multi-view consistency. In a nutshell, Endora marks a notable breakthrough in the deployment of generative AI for clinical endoscopy research, setting a substantial stage for further advances in medical content generation. Project page: https://endora-medvidgen.github.io/.
title: Endora: Video Generation Models as Endoscopy Simulators
authors: Li, Chenxin Liu, Hengyu Liu, Yifan Feng, Brandon Y. Li, Wuyang Liu, Xinyu Chen, Zhen Shao, Jing Yuan, Yixuan