Real-time distortion classification in laparoscopic videos



  • Prof. Azeddine Beghdadi, Universite Sorbonne Paris Nord, France
  • Dr. Mounir Kaaniche, Universite Sorbonne Paris Nord, France
  • Zohaib Amjad Khan, Universite Sorbonne Paris Nord, France
  • Prof. Faouzi Alaya Cheikh, Norwegian University of Science and Technology (NTNU), Norway
  • Prof. Bjørn Edwin, Oslo University Hospital, Norway
  • Prof. Ole Jakob Elle, Oslo University Hospital, Norway
  • Dr. Rafael Palomar, Oslo University Hospital, Norway
  • Egidijus Pelanis, Oslo University Hospital, Norway
  • Dr. Åsmund Avdem Fretland, Oslo University Hospital, Norway


Laparoscopic videos may be affected by different kinds of distortions during the surgery, resulting in a loss of visual quality. To prevent any disruptions in surgery due to video quality issues, there is a great need of having automated video enhancement systems. For any automated video enhancement system, the feedback loop plays an important part whereby any change in video quality is handled by applying the correct enhancement approach. In this feedback loop, one of the most critical steps is the identification of distortion affecting the video in real-time to allow timely application of enhancement. The purpose of this challenge is to target this problem by developing a fast, unified and effective algorithm for real-time classification of distortions within a laparoscopic video. In order to complete this challenge, we will provide our own dataset of shortduration laparoscopic videos called the Laparoscopic Video Quality (LVQ) database. These videos have been carefully selected from an existing public dataset and have been distorted with either single or multiple distortions simultaneously at different levels. In total, 800 such videos would be provided out of which a sample data of 200 is already available publicly.