1๏ธโฃ #ICRA2025 – “ETSM: Automating Dissection Trajectory Suggestion and Confidence Map-Based Safety Margin Prediction for Robot-assisted Endoscopic Submucosal Dissection”
This work introduces a framework for predicting optimal dissection trajectories while integrating a confidence map-based safety margin to minimize risks like tissue perforation.
๐ Key contributions:
– A novel dataset (ETSM), featuring over 1,800 annotated clips from robotic ESD procedures.
– The RCMNet model, which uses regression to predict confidence maps, guiding surgeons toward safer dissection zones.
– Outperformed baselines with a mean absolute error of 3.18, demonstrating robust performance even under visual challenges.
Authors: Mengya Xu, Wenjin Mo, Guankun Wang, Huxin Gao, An Wang, Long Bai, Chaoyang Lyu, Xiaoxiao Yang, Zhen Li, and Hongliang Ren
๐Paper link: https://lnkd.in/gUdr6XQc
2๏ธโฃ #IPCAI2025 – “PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection”
This paper adapts foundation models to enable region-specific dissection zone segmentation using flexible visual prompts (e.g., scribbles, bounding boxes).
๐ Key insights:
– A novel ESD-DZSeg dataset tailored for prompt-based segmentation tasks.
– Fine-tuned the foundation model DINOv2 with LoRA for efficient adaptation to surgical tasks. Achieved state-of-the-art accuracy, with long scribble prompts yielding a mean Intersection over Union (IoU) of 74.06%.
– Empowers surgeons to intuitively refine segmentation through natural visual cues, enhancing real-time decision support.
Authors: Mengya Xu, Wenjin Mo, Guankun Wang, Huxin Gao, An Wang, Zhen Li, Xiaoxiao Yang and Hongliang Ren
๐Paper link: https://lnkd.in/ggUM-5Zc
