Exploration of multiple imaging modalities for Glaucoma detection
Jahnavi Gamalapati S
Glaucoma is an eye disease characterized by weakening of nerve cells often resulting in a permanent loss of vision. Glaucoma progression can occur without any physical indication to patients. Hence, early diagnosis of Glaucoma is recommended for preventing the permanent damage to vision. Early Glaucoma is often characterized by thinning of the Retinal Nerve Fiber Layer which is commonly called as RNFL defect (RNFLD). Computer-aided diagnosis (CAD) of eye diseases is popular and is based on automated analysis of fundus images. CAD solutions for diabetic retinopathy have reached more maturity than for glaucoma as the latter is more difficult to detect from fundus images. This is due to the fact that nerve damage appears in the form of subtle change in the background around optic disc. SD-OCT (Spectral Domain - Optical Coherence Tomography), a recently introduced modality, helps to capture 3D information of retina. Hence, it is more reliable for detecting nerve damage in retina compared with fundus imaging. However, a wide usage of OCT is limited due to cost per scan, time and ease of acquisition. This thesis focuses on integrating information from multiple modalities (OCT and fundus) for improving retinal nerve fibre layer defect or detection of RNFLD from fundus images. We examine two key problems in the context of CAD development: i) spatial alignment or registration of two modalities of imaging, namely, 2D fundus and 3D OCT volume images. This can pave way to integrate information across the modalities. Multimodal registration is challenging because of the varied Field of View and noise levels across the modalities. We propose a computationally efficient registration algorithm which is capable of handling complementary nature of modalities. Extensive qualitative and quantitative evaluations are performed to show the robustness of proposed method. ii) Detection of RNFLD from fundus images with good accuracy. We propose a novel CAD solution which utilises information from the 2 modalities for learning a model and uses it to predict the presence of RNFLD from fundus images. The problem is posed as learning from 2 modalities (fundus, OCT images) and predicting from only one (fundus images) with the other (OCT) as missing data. Our solution consists of a deep neural network architecture which learn modality independent representations. In the final part of the thesis we explore the scope of a new imaging modality angiography-Optical Coherence Tomography (A-OCT) in diagnosing Glaucoma. Two case studies are reported which help in understanding the progression of Retinal Nerve Fiber Layer thickness, Capillary Density in normaland glaucoma effected patients. The experiments on new modality has shown potential for considering it as a reliable biomarker along with existing modalities.
|Year of completion:||July 2018|
|Advisor :||Jayanthi Sivaswamy|
Tarannum Mansoori, JS Gamalapati and Jayanthi Sivaswamy - Radial Peripapillary Capillary Density Measurement Using Optical Coherence Tomography Angiography in Early Glaucoma Journal of glaucoma 26.5 (2017): 438-443. [PDF]
Pujitha Appan K, Jahnavi Gamalapati S and Jayanthi Sivaswamy - Detection of neovascularization in retinal images using semi-supervised learning Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on. IEEE, 2017. [PDF]
T Mansoori, JS Gamalapati and Jayanthi Sivaswamy - Measurement of radial peripapillary capillary density in the normal human retina using optical coherence tomography angiography Journal of glaucoma 26.3 (2017): 241-246. [PDF]