Deep convolutional network based on rank learning for OCT retinal images quality assessment

Jia Yang Wang; Lei Zhang; Min Zhang; Jun Feng; Yi Lv M.D.

doi:10.1117/12.2513689

15 March 2019 Deep convolutional network based on rank learning for OCT retinal images quality assessment

Jia Yang Wang, Lei Zhang, Min Zhang, Jun Feng, Yi Lv M.D.

Proceedings Volume 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging; 1095309 (2019) https://doi.org/10.1117/12.2513689
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

The visual quality measurement of optical coherence tomography (OCT) images is very important for the diagnosis of diseases in the later stage. This paper presented a novel OCT image quality assessment method. The concept of pairwise learning in learning to rank (LTR) is introduced to extract image features sensitive to OCT image quality levels. First, a simple multi-input network (Ranking-based OCT image features extraction network) is constructed by using the residual structure. Second, the ROFE Network is trained by pairwise images. Third, the trained ROFE Network is used to extract the ranking sensitive features of OCT images. Finally, support vector regression (SVR) model is used to get the objective quality scores of OCT images. In order to verify the effectiveness of the proposed method, 608 OCT images with subjective perceptual quality are collected, and a number of experiments are carried out. The experimental results show the proposed method has strong correlations with subjective quality evaluations.

Citation Download Citation

Jia Yang Wang, Lei Zhang, Min Zhang, Jun Feng, and Yi Lv M.D. "Deep convolutional network based on rank learning for OCT retinal images quality assessment", Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1095309 (15 March 2019); https://doi.org/10.1117/12.2513689

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