SurgMotion is a video-native foundation model that replaces pixel-level reconstruction with latent motion prediction for surgical video analysis. It introduces motion-guided masked prediction, spatiotemporal affinity self-distillation, and spatiotemporal feature diversity regularization to focus on semantically meaningful regions and avoid representation collapse. Trained on SurgMotion-15M, the largest surgical video dataset, it outperforms state-of-the-art methods across 17 benchmarks, improving workflow recognition, action triplet recognition, skill assessment, polyp segmentation, and depth estimation.
By Jinlin Wu, Felix Holm, Chuxi Chen, An Wang, Yaxin Hu, Xiaofan Ye, Zelin Zang, Miao Xu, Lihua Zhou, Huai Liao, Danny T. M. Chan, Ming Feng, Wai S. Poon, Hongliang Ren, Dong Yi, Nassir Navab, Gaofeng Meng, Jiebo Luo, Hongbin Liu, Zhen Lei
arXiv:2607. 13237v1 Announce Type: cross Abstract: Precise spatial-temporal annotation of laparoscopic videos is time-consuming and requires expert knowledge.
By Manasa Dendukuri, Matjaz Jogan, Daniel A. Hashimoto, Guiqiu Liao
arXiv:2609.21402v1 Announce Type: new
Abstract: Surgical instrument segmentation (SIS) plays a critical role in robotic assistance and surgical workflow analysis. However, most existing SIS methods f...
By Zhibo Zhang, Qijie Wang, Zengqiang Yan
The paper introduces LaST, a large-small collaborative framework for zero-shot surgical phase recognition. It combines a foundation model that generates frame-level phase priors with a lightweight model that refines predictions through iterative temporal refinement, dynamic quality control, and dual-model cross-learning. Experiments show LaST outperforms baseline and state-of-the-art methods, achieving significant accuracy gains on unseen clinical domains.
By Yiyi Zhang, Ying Zheng, Wenxin Fan, Yu Zhu, Yuchen Yuan, Litao Zhao, Zheng Li, Pheng-Ann Heng
The paper presents a method for localizing functional surgical landmarks—specifically instrument tips and anchors—in surgical videos without requiring manual pixel-level mask annotations. It leverages vision foundation models, such as SAM 3, to generate dense structural priors through zero‑shot, point‑prompted masks, and refines landmark predictions with a lightweight, coarse‑to‑fine multi‑frame network. Experiments on 7,867 clips from 60 videos show that the approach achieves F1 scores of 72.4% for tip and 58.0% for anchor localization, with ablations confirming the benefits of structural priors and refinement stages.
By Chenyan Jing, Hao Ding, Lalithkumar Seenivasan, Jacob M. Delgado L\'opez, Mathias Unberath
arXiv:2608. 20284v1 Announce Type: new Abstract: Reliable surgical planning requires models to anticipate not only how instruments will move, but also how the operative visual state will evolve together with such motion.
By Weiliang Huang, Huanrong Liu, Bob Zhang, Qi Dou, Zhen Chen, Yun Gu, Guy Rosman, Qingbiao Li