The dataset is descriped in a pending publication titled "Cholec80-Boxes: Bounding-box Labeling Data for Surgical Tools in Cholecystectomy Images". The dataset was used in the following studies titled: "Surgical tool classification & localisation using attention and multi-feature fusion deep learning approach". "Laparoscopic video analysis using temporal, attention, and multi-feature fusion based-approaches". "Analysing attention convolutional neural network for surgical tool localisation: A feasibility study". The dataset consists of cholecystectomy images and bounding-box labels for surgical tools. These images were extracted from five videos of the Cholec80 dataset (Twinanda et al., 2016) at a rate of 1 Hz. The images are stored in '.png' format with a resolution of 854*480 pixels. Each video’s images are organized in a separate folder. The labeling data are stored in a CSV file, which contains the region of interest (ROI) labels for each surgical tool visible in the extracted images. Additionally, the CSV file provides information about each labeled image. Table 1 presents a content description of the 'ROI_Labels.csv' file. Table 1: Description of 'ROI_Labels.csv' file. Column Name Description Type Surgery_num Procedure number in the Cholec80 dataset from which the image was extracted. Integer Dir Directory of the image folder. String FrameName Image name in the format 'Video_SS_fffff.png', where SS is the Surgery_num and fffff is the frame number in the video. String NumBBox_inFrame The bounding-box number in the image. Integer ToolName Name of the surgical tool. String BBox_X X-coordinate of the top-left corner. Integer BBox_Y Y-coordinate of the top-left corner. Integer BBox_Width Bounding box width. Integer BBox_Height Bounding box height. Integer Citing This Dataset: When using this dataset, please cite the following publications: Jalal, N. A., Alshirbaji, T. A., Docherty, P. D., Arabian, H., Laufer, B., Krueger-Ziolek, S., Neumuth, T. & Moeller, K. (2023). Laparoscopic video analysis using temporal, attention, and multi-feature fusion based-approaches. Sensors, 23(4), 1958. Jalal, N. A., Alshirbaji, T. A., Docherty, P. D., Arabian, H., Neumuth, T., & Möller, K. (2023). Surgical tool classification & localisation using attention and multi-feature fusion deep learning approach. IFAC-PapersOnLine, 56(2), 5626-5631. Abdulbaki Alshirbaji, T., Arabian, H., Jalal, N. A., Battistel, A., Docherty, P. D., Neumuth, T., & Moeller, K. Cholec80-Boxes: Bounding-box labeling data for surgical tools in cholecystectomy images. (to be submitted). Twinanda, A. P., Shehata, S., Mutter, D., Marescaux, J., De Mathelin, M., & Padoy, N. (2016). Endonet: a deep architecture for recognition tasks on laparoscopic videos. IEEE transactions on medical imaging, 36(1), 86-97.