Deep Denoising of Volumetric OCT Images for In Vivo Motion Detection

Optical Coherence Tomography (OCT) is an imaging modality with high temporal and spatial resolution. There are different applications, e.g. in ophthalmology, dermatology, cardiology and recently OCT image based motion compensation has been considered. However, OCT suffers from speckle noise which affects image quality and subsequent interpretation. We propose denoising based on unsupervised 3D convolutional Autoencoder (AE) and systematically evaluate the model on in vivo and post mortem datasets. Moreover, we study motion detection as a subsequent task and to investigate the effects of AE based denoising. Our results demonstrate that speckle noise in the OCT images can lead to substantial outliers. Denoising based on AEs is effective in reducing the outliers and results in improved motion detection. Furthermore, the proposed AE processes OCT volumes in 0.5 ms, making it suitable for real-time applications. Finally, the results illustrate that the AE can effectively improve motion detection performance on in vivo data, despite being trained on different data. In conclusion, our AE model presents a simple and unsupervised deep learning approach to obtain fast denoising adapted to OCT imaging. For motion detection, denoising can be crucial to avoid artifacts due to outliers.Clinical relevance: This work provides an efficient denoising Autoencoder for fast data processing that can be applied in various clinical scenarios to improve noisy 3D Optical Coherence Tomography images.