Automatic Segmentation of Intra-Retinal Cysts from Optical Coherence Tomography Scans

Abstract

Retinal cysts are formed by accumulation of fluid in the retina caused by leakage due to blood retinal barrier breakdown from inflammation or vascular disorders. Analysis of retinal cystic spaces holds significance in detection, treatment and prognostication of several ocular diseases like age-related macular degeneration, diabetic macular edema, etc. Segmentation of intra-retinal cysts (IRCs) and their quantification is important for retinal pathology and severity characterization. In recent years, automated segmentation of intra-retinal cysts from optical coherence tomography (OCT) B-scans has gained significance in the field of retinal image analysis. In this thesis, a benchmark study is conducted to compare existing methods to identify the factors affecting IRC segmentation from OCT scans. A modular approach is employed to standardize the different IRC segmentation algorithms followed by analysis of variations in automated cyst segmentation performances and method scalability across image acquisition systems are done by using publicly available cyst segmentation challenge dataset (OPTIMA cyst segmentation challenge). Such exhaustive analysis on the scalability of OCT cyst segmentation methods in terms of methodological and input data variations has not been done before. An efficient cyst segmentation technique must be capable of performing cyst identification and delineation with minimum errors. Several methods proposed in the literature fail to delineate cysts up to their true boundary. To address this problem, an unsupervised vendor dependent method using marker controlled watershed transformation is proposed in this thesis. The method is based on two stages- k-means clustering technique is used to identify cysts in the form of marker, followed by topographical based watershed transform for final segmentation. Qualitative and quantitative evaluation of vthe proposed method is carried out against ground truth obtained from two graders on OPTIMA cyst segmentation challenge dataset (Spectralis Vendor OCT scans). Obtained results show that the proposed method outperformed other considered unsupervised methods. Several segmentation methods have been proposed in the literature for IRC segmentation on vendor-specific OCT images, but these lack generalizability across imaging systems. To address this issue, a fully convolutional network (FCN) model for vendor-independent IRC segmentation is proposed in this thesis. The proposed FCN was trained using the OPTIMA cyst segmentation challenge dataset (with four different vendor-specific images, namely, Cirrus, Nidek, Spectralis and Topcon). This method counteracts image noise variabilities and model over-fitting by data augmentation and hyper-parameter optimization. Additionally, sensitivity analysis of the model hyperparameters (depth and receptive field size) is performed to optimize the proposed FCN. The Dice Correlation Coefficient of the proposed method outperforms the algorithms published in the OPTIMA cyst segmentation challenge. Deeper FCNs exhibit better feature learning capabilities than shallower networks but those are computationally intensive due to large number of computation parameters and may be prone to vanishing gradient problem. To address this issue, a depthwise separable convolutional filter based end-end convolutional neural network architecture with swish activation functions is proposed in this thesis. OPTIMA cyst segmentation challenge dataset with four different vendor scans were used to evaluate the proposed architecture for vendor independent IRC segmentation task. Obtained experimental results show that the proposed method significantly reduced the number of computation parameters compared to regular convolution based FCN.