Effective Deep Learning Approach to Denoise Optical Coherence Tomography Images Using BM3D-Based Preprocessing of the Training Data Including Both Healthy and Pathological Cases

Retinal diseases are significant cause of visual impairment globally. In the worst case they may lead to severe vision loss or blindness. Accurate diagnosis is a key factor in the right treatment planning that can stop or slow the disease. The examination that can aid in the right diagnosis is Optical Coherence Tomography (OCT). OCT scans are susceptible to various noise effects which deteriorate their quality and as a result may impede the analysis of their content. In this paper, we propose a novel and effective method for OCT image denoising using a deep learning model trained on pairs of noisy and clean scans obtained by BM3D filtering. A comprehensive dataset of 21926 OCT scans, collected from 869 patients (1639 eyes), covering both healthy and pathological cases, was used for training and testing of the proposed scheme. The method was validated taking into account quantitative metrics concerning image quality. In addition, the proposed denoising scheme was evaluated by analyzing the impact of applying it in the eye disease classification based on Convolutional Neural Networks (CNNs) where we obtained the improvement of around 1–3 pp (percentage point). A separate dataset of 25697 scans collected from 1910 patients (2953 eyes) was used for this purpose. The conducted experiments have proved that the method can be applied as a preprocessing step in order to provide better disease classification results and can be useful in other OCT image analysis tasks. The proposed solution is much faster and perform better than the classical BM3D filter (over ninetyfold speed-up) and other related methods, especially when a big set of images needs to be processed at once. Furthermore, the use of the diverse dataset show the benefit over methods which are based on using only healthy scans for the training of the neural network.