Cholec80-Boxes: Bounding Box Labelling Data for Surgical Tools in Cholecystectomy Images
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 proced...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/2306-5729/10/1/7 |
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| author | Tamer Abdulbaki Alshirbaji Nour Aldeen Jalal Herag Arabian Alberto Battistel Paul David Docherty Hisham ElMoaqet Thomas Neumuth Knut Moeller |
| author_facet | Tamer Abdulbaki Alshirbaji Nour Aldeen Jalal Herag Arabian Alberto Battistel Paul David Docherty Hisham ElMoaqet Thomas Neumuth Knut Moeller |
| author_sort | Tamer Abdulbaki Alshirbaji |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b16623776b4f4ee28785da6ba81f9aa4 |
| institution | Kabale University |
| issn | 2306-5729 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj-art-b16623776b4f4ee28785da6ba81f9aa42025-01-24T13:28:32ZengMDPI AGData2306-57292025-01-01101710.3390/data10010007Cholec80-Boxes: Bounding Box Labelling Data for Surgical Tools in Cholecystectomy ImagesTamer Abdulbaki Alshirbaji0Nour Aldeen Jalal1Herag Arabian2Alberto Battistel3Paul David Docherty4Hisham ElMoaqet5Thomas Neumuth6Knut Moeller7Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, GermanyInnovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, 04103 Leipzig, GermanyInstitute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, GermanyInstitute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, GermanyInstitute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, GermanyDepartment of Mechatronics Engineering, German Jordanian University, Amman 11180, JordanInnovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, 04103 Leipzig, GermanyInstitute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, GermanySurgical 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.https://www.mdpi.com/2306-5729/10/1/7surgical tool detectionlaparoscopic imagesbounding box label |
| spellingShingle | Tamer Abdulbaki Alshirbaji Nour Aldeen Jalal Herag Arabian Alberto Battistel Paul David Docherty Hisham ElMoaqet Thomas Neumuth Knut Moeller Cholec80-Boxes: Bounding Box Labelling Data for Surgical Tools in Cholecystectomy Images Data surgical tool detection laparoscopic images bounding box label |
| title | Cholec80-Boxes: Bounding Box Labelling Data for Surgical Tools in Cholecystectomy Images |
| title_full | Cholec80-Boxes: Bounding Box Labelling Data for Surgical Tools in Cholecystectomy Images |
| title_fullStr | Cholec80-Boxes: Bounding Box Labelling Data for Surgical Tools in Cholecystectomy Images |
| title_full_unstemmed | Cholec80-Boxes: Bounding Box Labelling Data for Surgical Tools in Cholecystectomy Images |
| title_short | Cholec80-Boxes: Bounding Box Labelling Data for Surgical Tools in Cholecystectomy Images |
| title_sort | cholec80 boxes bounding box labelling data for surgical tools in cholecystectomy images |
| topic | surgical tool detection laparoscopic images bounding box label |
| url | https://www.mdpi.com/2306-5729/10/1/7 |
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