A neural network based framework for effective laparoscopic video quality assessment. (October 2022)

• Record Type: Journal Article
• Authors: Khan, Zohaib Amjad; Beghdadi, Azeddine; Kaaniche, Mounir; Alaya-Cheikh, Faouzi; Gharbi, Osama
• Is Part Of: Computerized medical imaging and graphics. Volume 101(2022)
• Publication Date: 2022-10
• Subjects: Video quality assessment -- Distortion classification -- Quality prediction -- Residual networks -- Fully connected neural network -- End-to-end learning -- Video guided surgery; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2022.102121
• Languages: English
• ISSNs: 0895-6111

Abstract

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. Highlights: A new blind quality assessment method for laparoscopic videos using neural networks. Simultaneous score and distortion prediction unlike existing NN VQA methods. A novel and effective temporal aggregation based on Fully Connected Neural Network. The proposed method with an end-to-end learning outperforms state-of-the-art methods.