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This is the publicly available dataset from robot-assisted laparoscopic hysterectomy surgery providing a benchmark for designing and validating smoke removal algorithms.

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Overview

The laparoscopic surgery dataset is associated with our International Journal of Computer Assisted Radiology and Surgery (IJCARS) publication titled . . We propose DeSmoke-LAP, a new method for removing smoke from real robotic laparoscopic hysterectomy videos. The proposed method is based on the unpaired image-to-image cycle-consistent generative adversarial network in which two novel loss functions, namely, inter-channel discrepancies and dark channel prior.

The dataset contains frames and video clips from 10 robot-assisted laparoscopic hysterectomy procedure videos. The original videos were decomposed into frames at 1 fps. From each video, 300 hazy images and 300 clear images were manually selected by observing the electrocauterisation. A short video clip of 50 frames from each procedure was also selected that was utilised for testing. 5-fold cross-validation was performed for all methods under comparison. Quantitative evaluation was done using referenceless metrics and qualitative evaluation was performed through a survey filled out by end-users (surgeons).

Downloading the Dataset

This dataset has been released.

License

The DeSmoke-LAP Dataset is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International ().

Citing the Dataset

Please cite the following publication whenever research making use of this dataset is reported in any academic publication or research report:

Contact

For comments, suggestions or feedback, or if you experience any problems with this website or the dataset, please contact Yirou Pan and Sophia Bano. To find out more about our research team, visit the Surgical Robot Vision and Wellcome / EPSRCÌýCentre for Interventional and Surgical Science websites.