Retinal Image Synthesis

Anurag Anil Deshmukh

Abstract

Medical imaging has been aiding diagnosis and treatment of diseases by creating visual representations of the interior of the human body. Experts hand-mark these images for abnormalities and diagnosis. Supplementing experts with these rich visualization has enabled detailed clinical analysis and rapid medical intervention. However, deep learning-based methods rely on abundantly large volumes of data for training. Procuring data for medical imaging applications is especially difficult because abnormal cases by definition are rare and the data, in general, requires experts for labelling. With Deep learning algorithms, data with high class imbalance or of insufficient variability leads to poor classification performance. Thus, alternate approaches like using generative modelling to artificially generate more data have been of interest. Most of these methods are GAN [11] based approaches. While they can be helpful with data imbalance they still require a lot of data to be able to generate realistic images. Additionally, a lot of these methods have been shown to work on natural images where the images are relatively noise-free and smaller artifacts aren’t as damaging. Thus, this thesis aims at providing synthesis methods which overcome the limitations of smaller datasets and noisy profile. We do this for two different modalities, Fundus imaging and Optical Coherence Tomography (OCT). Firstly, we present a fundus image synthesis method aimed at providing paired Optic Cup and Image data for Optic Cup (OC) Segmentation. The synthesis method works well on small datasets by minimising the information to be learnt by leveraging domain-specific knowledge and by providing most of the structural information to the network. We demonstrate this method’s advantages over a more direct synthesis method. We show how leveraging domain-specific knowledge can provide higher quality images and annotations. Inclusion of these generated images and their annotations in training of an OC segmentation model showed a significant improvement in performance, thus showing their reliability. Secondly, we present a novel unpaired image to image translation method which can introduce abnormality (Drusen) to OCT images while avoiding artifacts and preserving the noise profile. Comparison with other state-of-the-art images to image translation methods shows that our method is significantly better at preserving the noise profile and is better at generating morphologically accurate structures.

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