A Neural Network based Framework for Effective Laparoscopic Video Quality Assessment

TL;DR

This paper introduces neural network models, including ResNet and FCNN, for classifying and predicting the quality of laparoscopic videos affected by distortions, aiming to improve surgical video assessment.

Contribution

It presents a novel neural network framework combining ResNet and FCNN for laparoscopic video quality assessment, utilizing transfer and end-to-end learning techniques.

Findings

Proposed models outperform recent methods in accuracy.

Effective classification and quality prediction on a new laparoscopic video database.

Demonstrated the benefit of transfer learning in medical video quality assessment.

Abstract

Video quality assessment is a challenging problem having a critical significance in the context of medical imaging. For instance, in laparoscopic surgery, the acquired video data suffers from different kinds of distortion that not only hinder surgery performance but also affect the execution of subsequent tasks in surgical navigation and robotic surgeries. For this reason, we propose in this paper neural network-based approaches for distortion classification as well as quality prediction. More precisely, a Residual Network (ResNet) based approach is firstly developed for simultaneous ranking and classification task. Then, this architecture is extended to make it appropriate for the quality prediction task by using an additional Fully Connected Neural Network (FCNN). To train the overall architecture (ResNet and FCNN models), transfer learning and end-to-end learning approaches are investigated. Experimental results, carried out on a new laparoscopic video quality database, have shown the efficiency of the proposed methods compared to recent conventional and deep learning based approaches.