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

Tamer Abdulbaki Alshirbaji, Nour Aldeen Jalal, Herag Arabian, Alberto Battistel (), Paul David Docherty, Hisham ElMoaqet, Thomas Neumuth and Knut Moeller
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Tamer Abdulbaki Alshirbaji: Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, 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
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

Data, 2025, vol. 10, issue 1, 1-9

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.

Keywords: surgical tool detection; laparoscopic images; bounding box label (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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