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Anatomical Structure Segmentation in Retinal Images with Some Applications in Disease DetectionAuthor: Arunava Chakravarty Date: 2019-11-25 Report no: IIIT/TH/2019/123 Advisor:Jayanthi Sivaswamy AbstractColor Fundus (CF) imaging and Optical Coherence Tomography (OCT) are widely used by ophthalmologists to visualize the retinal surface and the intra-retinal tissue layers respectively. An accurate segmentation of the anatomical structures in these images is necessary to visualize and quantify the structural deformations that characterize retinal diseases such as Glaucoma, Diabetic Macular Edema (DME) and Age-related Macular Degeneration (AMD). In this thesis, we propose different frameworks for the automatic extraction of the boundaries of relevant anatomical structures in CF and OCT images. First, we address the problem of the segmentation of Optic Disc (OD) and Optic Cup (OC) in CF images to aid in the detection of Glaucoma. We propose a novel boundary-based Conditional Random Field (CRF) framework to jointly extract both the OD and OC boundaries in a single optimization step. Although OC is characterized by the relative drop in depth from the OD boundary, the 2D CF images lack explicit depth information. The proposed method estimates depth from CF images in a supervised manner using a coupled, sparse dictionary trained on a set of image-depth map (derived from OCT) pairs. Since our method requires a single CF image per eye during testing it can be employed in the large-scale screening of glaucoma where expensive 3D imaging is unavailable. Next, we consider the task of the intra-retinal tissue layer segmentation in cross-sectional OCT images which is essential to quantify the morphological changes in specific tissue layers caused by AMD and DME. We propose a supervised CRF framework to jointly extract the eight layer boundaries in a single optimization step. In contrast to the existing energy mini-mization based segmentation methods that employ handcrafted energy cost terms, we linearly parameterize the total CRF energy to allow the appearance features for each layer and the relative weights of the shape priors to be learned in a joint, end-to-end manner by employing the Structural Support Vector Machine formulation. The proposed method can aid the oph-thalmologists in the quantitative analysis of structural changes in the retinal tissue layers for clinical practice and large-scale clinical studies. Next, we explore the Level Set based Deformable Models (LDM) which is a popular energy minimization framework for medical image segmentation. We model the LDM as a novel Recurrent Neural Network (RNN) architecture called the Recurrent Active Contour Evolution Network (RACE-net). In contrast to the existing LDMs, RACE-net allows the curve evolution velocities to be learned in an end-to-end manner while minimizing the number of network parameters, computation time and memory requirements. Consistent performance of RACE-net on a diverse set of segmentation tasks such as the extraction of OD and OC in CF images, cell nuclei in histopathological images and left atrium in cardiac MRI volumes demonstrates its utility as a generic, off-the-shelf architecture for biomedical segmentation. Segmentation has many clinical applications especially in the area of computer aided diagnostics. We close this dissertation with some illustrative applications of the segmentation information. We consider the case of disease detection in CF and OCT images. We explore and benchmark two classification strategies for the detection of glaucoma from CF images based on deep learning and handcrafted features respectively. Both the methods use a combination of appearance features directly derived from the CF image and structural features derived from the OD and OC segmentation. We also construct a Normative Atlas for the macular OCT volumes to aid in the detection of AMD. The irregularities in the Bruch’s membrane caused by the deposit of drusen are modeled as deviations from the normal anatomy represented by the Atlas Mean Template Full thesis: pdf Centre for Visual Information Technology |
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