DeSmoke-LAP dataset

The laparoscopic surgery dataset is associated with our International Journal of Computer Assisted Radiology and Surgery (IJCARS) publication titled “DeSmoke-LAP: Improved Unpaired Image-to-Image Translation for Desmoking in Laparoscopic Surgery”. The training model of the proposed method is available as an open source on Github. 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).