Deep residual-network-based quality assessment for SD-OCT retinal images: Preliminary study

Optical coherence tomography (OCT) is widely used as an imaging technique for in vivo imaging of the human retina in clinical 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 been proposed, their performance still needs to be improved. To the best of our knowledge, there is still no OCT image quality assessment algorithm based on deep learning framework. To address the OCT image quality assessment issue, we proposed an objective OCT image quality assessment (IQA) using Residual Networks (ResNets) combined with support vector regression (SVR) in this paper. A dataset of 482 OCT images is constructed, and the images quality are scored by the clinic experts. The pre-trained deep residual network from ImageNet is slightly revised and then fine-tuned to extract the features from OCT images. Then, the extracted features from the images and their corresponding subjective rating scores are used to learn the non-linear map with Support Vector Regression(SVR). To evaluate the performance of the proposed 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 quality assessment.