README.md

metadata

title: SurgiTrack - Surgical Tool Tracking
emoji: 🔬
colorFrom: purple
colorTo: indigo
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: mit

SurgiTrack - Surgical Tool Tracking

Multi-class multi-tool tracking system for laparoscopic surgery videos.

Overview

This demo implements the tracking pipeline from "SurgiTrack: Fine-Grained Multi-Class Multi-Tool Tracking in Surgical Videos", trained and evaluated on the CholecTrack20 dataset.

Pipeline

1. Detection: YOLOv11x trained on 7 surgical tool classes
2. Direction Estimation: EfficientNet-B0 + Coordinate Attention predicts operator (MSLH, MSRH, ASRH)
3. Tracking: Operator-based slot assignment for graspers, fixed IDs for other tools

Results

Metric	Score
HOTA	64.48%
AssA	71.19%
DetA	58.51%

Tool Classes

• Grasper (tracked by operator)
• Bipolar
• Hook
• Scissors
• Clipper
• Irrigator
• Specimen Bag

Citation

@InProceedings{nwoye2023cholectrack20,
  author    = {Nwoye, Chinedu Innocent and Elgohary, Kareem and Srinivas, Anvita and Zaid, Fauzan and Lavanchy, Joël L. and Padoy, Nicolas},
  title     = {CholecTrack20: A Multi-Perspective Tracking Dataset for Surgical Tools},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2025},
  month     = {June}
}

Author

Djalil Khelladi