Published September 2, 2024 | Version v1
Dataset Open

Cholec80-Boxes: Bounding-Box Labels for Surgical Tools in Five Cholecystectomy Videos

Authors/Creators

  • 1. ROR icon Furtwangen University
  • 2. ROR icon Leipzig University
  • 3. University of Canterbury
  • 4. ROR icon German Jordanian University

Description

 

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.

Files

ROI_Labels.csv

Files (6.5 GB)

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md5:85942f10bbcad04fd6d11ba550240e8c
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md5:d762288d1849b64b0226f31d2485e9de
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Additional details

Related works

Is derived from
Journal article: 10.1109/TMI.2016.2593957 (DOI)
Is described by
Data paper: 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). (Other)
Is documented by
Journal article: 10.3390/s23041958 (DOI)
Journal article: 10.1016/j.ifacol.2023.10.473 (DOI)
Conference paper: 10.1515/cdbme-2022-1140 (DOI)

Funding

Federal Ministry of Education and Research
CoHMed/PersonaMed-B 13FH5I09IA
German Academic Exchange Service
AIDE-ASD FKZ 57656657

References

  • 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.
  • 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., Arabian, H., Abdulbaki Alshirbaji, T., Docherty, P., Neumuth, T. & Moeller, K. (2022). Analysing attention convolutional neural network for surgical tool localisation: a feasibility study. Current Directions in Biomedical Engineering, 8(2), 548-551.
  • 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).
  • 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.