Surgical instrument segmentation (SIS) is fundamental for computer-assisted surgery, where reliable instrument masks enable precise scene understanding and clinical assistance. Recently, adapting foun...
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.
arXiv:2607. 29509v1 Announce Type: cross Abstract: Effective multi-organ segmentation in surgical data requires learning the intricate anatomical features and alleviating the challenge of class imbalance, which results from relatively lower proportions of small and limitedly exposed structures.
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...
arXiv:2610.34925v1 Announce Type: new Abstract: In surgical instrument segmentation, the increasing variety of instruments over time poses a significant challenge for existing neural networks, as the...
arXiv:2505.03380v2 Announce Type: replace Abstract: Accurate delineation of tumors and surrounding organs-at-risk is essential for radiotherapy, surgery and treatment response assessment, yet remains...
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.
arXiv:2607.12896v3 Announce Type: replace Abstract: Medical image segmentation foundation models are expected to generalize across diverse clinical scenarios, yet existing universal methods remain fr...
arXiv:2610.34210v1 Announce Type: cross Abstract: Modern surgical AI increasingly requires multimodal understanding rather than isolated perception, yet existing datasets remain largely limited to si...
CasCVS‑Net is a staged multi‑task cascade that jointly performs object detection, semantic segmentation, and Critical View of Safety (CVS) assessment for laparoscopic cholecystectomy. The model couples tasks through predicted anatomy—boxes guide segmentation and masks provide region‑level features for CVS classification—allowing CVS assessment to rely solely on model predictions. Trained on the Endoscapes dataset, CasCVS‑Net outperforms state‑of‑the‑art methods, achieving higher mAP and mIoU scores across detection, segmentation, and CVS tasks, especially for rare hepatocystic structures.
arXiv:2609.25850v1 Announce Type: new Abstract: Deep learning performance generally improves with increasing training data, yet this scaling is fundamentally constrained by annotation cost in large-s...
Effective multi-task learning for surgical scene understanding is fundamentally hindered by annotation granularity mismatch; temporal workflow tasks such as phase recognition, step recognition and anticipation benefit from dense frame-level supervision, whereas pixel-level spatial tasks including instrument segmentation and action recognition are only sparsely annotated on selected keyframes due to prohibitive labeling costs. This supervision imbalance undermines shared representation learning and limits joint optimization across heterogeneous surgical tasks.