Retinal Image Quality Improvement via Learning
Sukesh Adiga V
Retinal images are widely used to detect and diagnose many diseases such as Diabetic Retinopathy (DR), glaucoma, Age-related Macular Degeneration, Cystoid Macular Edema, coronary heart disease,and so on. These diseases affect vision and lead to irreversible blindness. Early image-based screening and monitoring of the patient is a solution. Imaging of retina is commonly done either through Optical Coherence Tomography (OCT) or Fundus photography. OCT captures cross-sectional information about the retinal layers in a 3D volume, whereas fundus imaging projects retinal tissues onto the 2D imaging plane. Recently smartphone camera-based fundus imaging is being explored with a relatively low-cost. Imaging retina with these technologies pose challenges due to physical properties of the light source, or quality of optics and sensors used or low and uneven light condition. In this thesis, we look at learning based approaches, namely neural network techniques to improve the quality of retinal images to aid diagnosis. The first part of this thesis aims at denoising OCT images, which are corrupted by speckle noise due to underlying coherence-based imaging technique. We propose a new method for denoising OCT images based on Convolutional Neural Network by learning common features from unpaired noisy and clean OCT images in an unsupervised, end-to-end manner. The proposed method consists of a combination of two autoencoders with shared encoder layers, which we call as Shared Encoder (SE) architecture. The SE is trained to reconstruct noisy and clean OCT images with respective autoencoders, and denoised OCT image is obtained using a cross-model prediction. The proposed method can be used for denoising OCT images with or without pathology from any scanner. The SE architecture was assessed using public datasets and found to perform better than baseline methods exhibiting a good balance of retaining anatomical integrity and speckle reduction. The second problem we focus on is the enhancement of fundus images acquired with a Smartphone camera (SC). SC image is a cost-effective solution for the assessment of retina, especially in screening. However, imaging at high magnification and low light levels results in loss of details, uneven illumination, noise particularly in the peripheral region and flash-induced artefacts. We address these problems by matching the characteristics of images from SC to those from a regular fundus camera (FC) using either unpaired or paired data. Two mapping solutions are designed using deep learning technique in an unsupervised and supervised manner. The unsupervised architecture called ResCycleGAN is based on the CycleGAN with two significant changes: A residual connection is introduced to aid learning only the correction required; A structure similarity based loss function is used to improve the clarity of anatomical structures and pathologies. This method can handle variations seen in normal and pathological images, acquired even without mydriasis, which is attractive in screening. The method produces consistently balanced results, outperforms CycleGAN both qualitatively and quantitatively, and has more pleasing results. Next, a new architecture is proposed called SupEnh, which handles noise removal using paired data. The proposed method enhances the quality of SC images along with denoising in an end-to-end, supervised manner. Obtaining paired data is challenging; however, it is feasible in fixed clinical settings or commercial product as it is required once for learning. The proposed SupEnh method based on U-net consists of an encoder and two decoders. The network simplifies the task by learning denoising and mapping to FC separately with two decoders. The method handles images with/without pathologies as well as images acquired even without mydriasis. The SupEnh was assessed using private datasets and found to performs better than U-net. The cross-validation results show method is robust to change in image quality. The enhancement using SupEnh method achieves 5% higher AUC for early stage DR detection when compared with original images.
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