# Speak, Segment, Track, Navigate: An Interactive System for Video-Guided Skull-Base Surgery

arXiv: https://arxiv.org/abs/2603.16024 · HTML: https://mlsanity.com/paper/2603.16024/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 31 references that are in the map.
- First posted on arXiv 17 Mar 2026.

## Builds on (most cited first, 19 of 19)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://mlsanity.com/paper/2401.14159/) (2024)
- [FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects](https://mlsanity.com/paper/2312.08344/) (2023)
- [Putting the Object Back into Video Object Segmentation](https://mlsanity.com/paper/2310.12982/) (2023)
- [FoundationStereo: Zero-Shot Stereo Matching](https://mlsanity.com/paper/2501.09898/) (2025)
- [ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation](https://mlsanity.com/paper/2203.09418/) (2022)
- [SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation](https://mlsanity.com/paper/2308.08746/) (2023)
- [IGEV++: Iterative Multi-range Geometry Encoding Volumes for Stereo Matching](https://mlsanity.com/paper/2409.00638/) (2024)
- [CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer Assisted Interventions](https://mlsanity.com/paper/1910.09031/) (2019)
- [LLaVA-Surg: Towards Multimodal Surgical Assistant via Structured Surgical Video Learning](https://mlsanity.com/paper/2408.07981/) (2024)
- [Core Challenges in Embodied Vision-Language Planning](https://mlsanity.com/paper/2106.13948/) (2021)
- [VS-Assistant: Versatile Surgery Assistant on the Demand of Surgeons](https://mlsanity.com/paper/2405.08272/) (2024)
- [SurgBox: Agent-Driven Operating Room Sandbox with Surgery Copilot](https://mlsanity.com/paper/2412.05187/) (2024)
- [SuFIA: Language-Guided Augmented Dexterity for Robotic Surgical Assistants](https://mlsanity.com/paper/2405.05226/) (2024)
- [TAToo: Vision-based Joint Tracking of Anatomy and Tool for Skull-base Surgery](https://mlsanity.com/paper/2212.14131/) (2022)
- [SCOPE: Speech-guided COllaborative PErception Framework for Surgical Scene Segmentation](https://mlsanity.com/paper/2509.10748/) (2025)

## Built on by (most cited first, 1 of 1)

- [Latent-Action-Guided Video-Language Feature Learning for Surgical Instrument-Tissue Interaction Recognition](https://mlsanity.com/paper/2607.19889/) (2026)

## Abstract (arXiv)

We introduce a speech-guided embodied agent framework for video-guided skull base surgery that dynamically executes perception and image-guidance tasks in response to surgeon queries. The proposed system integrates natural language interaction with real-time visual perception directly on live intraoperative video streams, thereby enabling surgeons to request computational assistance without disengaging from operative tasks. Unlike conventional image-guided navigation systems that rely on external optical trackers and additional hardware setup, the framework operates purely on intraoperative video. The system begins with interactive segmentation and labeling of the surgical instrument. The segmented instrument is then used as a spatial anchor that is autonomously tracked in the video stream to support downstream workflows, including anatomical segmentation, interactive registration of preoperative 3D models, monocular video-based estimation of the surgical tool pose, and image guidance through real-time anatomical overlays. We evaluate the proposed system in video-guided skull base surgery scenarios and benchmark its tracking performance against a commercially available optical tracking system. Across three experimental trials, the hybrid vision-based method achieved a mean absolute tool-tip position error of 2.32 Plus/Minus 1.10 mm in the camera frame, with inter-frame yaw and pitch propagation discrepancies of 0.18 Plus/Minus 0.25deg and 0.21 Plus/Minus 0.30deg, respectively. The system completes tool segmentation and anatomy registration within approximately two minutes, substantially reducing setup complexity relative to conventional tracking workflows. These results demonstrate that speech-guided embodied agents can provide accurate spatial guidance while improving workflow integration and enabling rapid deployment of video-guided surgical systems.