Deep Residual Network Based Quality Assessment for SD-OCT Retinal Images: Preliminary Study

摘要

Optical coherence tomography (OCT) is widely used as an imaging technique for in vivo imaging of the human retina inclinical ophthalmology. For reliable clinical measurements, the quality of the OCT images needs to be sufficient. Hence,quality evaluation of OCT images is necessary. Although some quality assessment algorithms for OCT images have beenproposed, their performance still needs to be improved. To the best of our knowledge, there is still no OCT image qualityassessment algorithm based on deep learning framework. To address the OCT image quality assessment issue, weproposed an objective OCT image quality assessment (IQA) using Residual Networks (ResNets) combined with supportvector regression (SVR) in this paper. A dataset of 482 OCT images is constructed, and the images quality are scored bythe clinic experts. The pre-trained deep residual network from ImageNet is slightly revised and then fine-tuned to extractthe features from OCT images. Then, the extracted features from the images and their corresponding subjective ratingscores are used to learn the non-linear map with Support Vector Regression(SVR). To evaluate the performance of theproposed method, the correlation coefficients between the predicted score and the subjective rating score are utilized.And the experimental result demonstrates that the proposed algorithm is highly efficient in the OCT image qualityassessment.