Full Field Optical Coherence Tomography Image Denoising Using Deep Learning with Spatial Compounding

In recent years, deep learning is widely and successfully applied in the medical images which have been established an abundant database in clinical practice. OCT is a relatively new imaging technique and worth in-depth exploration in the deep learning field, however, it is still in an early stage where medical doctors are learning to interpret its images. For shortening the learning curve, this paper used a deep convolutional neural network on a high-resolution full-field OCT system to enhance features in images. By combining with the spatial compounding technique, a noise map prediction method can be employed to discriminate noises from signals and thus increase the image quality. For 100 testing samples, the average of PSNR and SSIM have improved from 20.7 and 0.43 to 26.55 and 0.68 after denoising by the proposed denoising model. Moreover, some important features would be more distinct to support diagnosis in clinical data.