Three-dimensional Optical Coherence Tomography Image Denoising through Multi-input Fully-Convolutional Networks
This addresses noise reduction in OCT imaging, a domain-specific problem for medical diagnostics, with an incremental approach building on existing CNN methods.
The paper tackles denoising in optical coherence tomography (OCT) images, which are affected by noise due to coherent image formation, by proposing a multi-input fully-convolutional network (MIFCN) that exploits correlations among neighboring images, achieving results compared quantitatively and qualitatively with state-of-the-art methods on OCT images of normal and diseased eyes.
In recent years, there has been a growing interest in applying convolutional neural networks (CNNs) to low-level vision tasks such as denoising and super-resolution. Due to the coherent nature of the image formation process, optical coherence tomography (OCT) images are inevitably affected by noise. This paper proposes a new method named the multi-input fully-convolutional networks (MIFCN) for denoising of OCT images. In contrast to recently proposed natural image denoising CNNs, the proposed architecture allows the exploitation of high degrees of correlation and complementary information among neighboring OCT images through pixel by pixel fusion of multiple FCNs. The parameters of the proposed multi-input architecture are learned by considering the consistency between the overall output and the contribution of each input image. The proposed MIFCN method is compared with the state-of-the-art denoising methods adopted on OCT images of normal and age-related macular degeneration eyes in a quantitative and qualitative manner.