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CholecTrack20 is a surgical video dataset focusing on laparoscopic cholecystectomy and designed for surgical tool tracking, featuring 20 annotated videos. The dataset includes detailed labels for multi-class multi-tool tracking, offering trajectories for tool visibility within the camera scope, intracorporeal movement within the patient's body, and the life-long intraoperative trajectory of each tool. Annotations cover spatial coordinates, tool class, operator identity, phase, visual conditions (occlusion, bleeding, smoke), and more for tools like grasper, bipolar, hook, scissors, clipper, irrigator, and specimen bag, with annotations provided at 1 frame per second across 35K frames and 65K instance tool labels. The dataset uses official splits, allocating 10 videos for training, 2 for validation, and 8 for testing.
@misc{nwoye2023cholectrack20,
title={CholecTrack20: A Dataset for Multi-Class Multiple Tool Tracking in Laparoscopic Surgery},
author={Chinedu Innocent Nwoye and Kareem Elgohary and Anvita Srinivas and Fauzan Zaid and Joël L. Lavanchy and Nicolas Padoy},
year={2023},
eprint={2312.07352},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Metric evaluation part of the codes are borrowed from TrackEval and Cocoapi. Thanks for their excellent work!
We welcome contributions of new metrics and new supported benchmarks. Also any other new features or code improvements. Send a PR, an email, or open an issue detailing what you'd like to add/change to begin a conversation.