Self-supervised speckle noise reduction of optical coherence tomography without clean data

Abstract

Optical coherence tomography (OCT) is widely used in clinical diagnosis due to its non-invasive, real-time, and high-resolution characteristics. However, the inherent speckle noise seriously degrades the image quality, which might damage the fine structures in OCT, thus affecting the diagnosis results. In recent years, supervised deep learning-based denoising methods have shown excellent denoising ability. To train a deep denoiser, a large number of paired noisy-clean images are required, which is difficult to achieve in clinical practice, since acquiring a speckle-free OCT image requires dozens of repeated scans and image registration. In this research, we propose a self-supervised strategy that helps build a despeckling model by training it to map neighboring pixels in a single noisy OCT image. Adjacent pixel patches are randomly selected from the original OCT image to generate two similar undersampled images, which are respectively used as the input and target images for training a deep neural network. To ensure both the despeckling and the structure-preserving effects, a multi-scale pixel patch sampler and corresponding loss functions are adopted in our practice. Through quantitative evaluation and qualitative visual comparison, we found that the proposed method performs better than state-of-the-art methods regarding despeckling effects and structure preservation. Besides, the proposed method is much easier to train and deploy without the need for clean OCT images, which has great significance in clinical practice.