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A neural network based framework for effective laparoscopic video quality assessment.

Zohaib Amjad Khan1, Azeddine Beghdadi1, Mounir Kaaniche1

  • 1Université Sorbonne Paris Nord, L2TI, UR 3043, F-93430, Villetaneuse, France.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|September 29, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces neural network methods for assessing video quality in laparoscopic surgery. The proposed approaches effectively classify distortions and predict quality, improving surgical navigation and robotic procedures.

Keywords:
Distortion classificationEnd-to-end learningFully connected neural networkQuality predictionResidual networksVideo guided surgeryVideo quality assessment

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Video quality assessment is crucial in medical imaging, particularly for laparoscopic surgery.
  • Distortions in surgical videos can impair surgical performance and subsequent robotic or navigation tasks.
  • Existing methods may not adequately address the complexities of surgical video quality.

Purpose of the Study:

  • To develop and evaluate neural network-based approaches for distortion classification and quality prediction in laparoscopic surgical videos.
  • To enhance the reliability of video data for surgical navigation and robotic surgery applications.
  • To investigate the effectiveness of transfer learning and end-to-end learning for training these models.

Main Methods:

  • A Residual Network (ResNet) was developed for simultaneous video distortion ranking and classification.
  • The ResNet architecture was extended with a Fully Connected Neural Network (FCNN) for video quality prediction.
  • Transfer learning and end-to-end learning strategies were employed for model training.
  • Experiments were conducted on a novel laparoscopic video quality database.

Main Results:

  • The proposed ResNet and FCNN models demonstrated high efficiency in classifying distortions and predicting video quality.
  • The methods showed superior performance compared to existing conventional and deep learning approaches.
  • The trained models effectively addressed the challenges posed by surgical video distortions.

Conclusions:

  • Neural network-based methods, specifically ResNet and FCNN, offer a robust solution for laparoscopic video quality assessment.
  • These advanced techniques can significantly improve the usability of surgical video data for critical applications.
  • The developed approaches provide a valuable tool for enhancing surgical precision and safety.