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Cholec80-Boxes: Bounding Box Labelling Data for Surgical Tools in Cholecystectomy Images

Author

Listed:
  • Tamer Abdulbaki Alshirbaji

    (Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany
    Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, 04103 Leipzig, Germany)

  • Nour Aldeen Jalal

    (Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, 04103 Leipzig, Germany)

  • Herag Arabian

    (Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany)

  • Alberto Battistel

    (Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany)

  • Paul David Docherty

    (Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany
    Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand)

  • Hisham ElMoaqet

    (Department of Mechatronics Engineering, German Jordanian University, Amman 11180, Jordan)

  • Thomas Neumuth

    (Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, 04103 Leipzig, Germany)

  • Knut Moeller

    (Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany)

Abstract

Surgical data analysis is crucial for developing and integrating context-aware systems (CAS) in advanced operating rooms. Automatic detection of surgical tools is an essential component in CAS, as it enables the recognition of surgical activities and understanding the contextual status of the procedure. Acquiring surgical data is challenging due to ethical constraints and the complexity of establishing data recording infrastructures. For machine learning tasks, there is also the large burden of data labelling. Although a relatively large dataset, namely the Cholec80, is publicly available, it is limited to the binary label data corresponding to the surgical tool presence. In this work, 15,691 frames from five videos from the dataset have been labelled with bounding boxes for surgical tool localisation. These newly labelled data support future research in developing and evaluating object detection models, particularly in the laparoscopic image data analysis domain.

Suggested Citation

  • Tamer Abdulbaki Alshirbaji & Nour Aldeen Jalal & Herag Arabian & Alberto Battistel & Paul David Docherty & Hisham ElMoaqet & Thomas Neumuth & Knut Moeller, 2025. "Cholec80-Boxes: Bounding Box Labelling Data for Surgical Tools in Cholecystectomy Images," Data, MDPI, vol. 10(1), pages 1-9, January.
    • Handle: RePEc:gam:jdataj:v:10:y:2025:i:1:p:7-:d:1562867
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