Automated diagnosis of macular edema and central serous retinopathy through robust reconstruction of 3D retinal surfaces. (December 2016)

• Record Type: Journal Article
• Authors: Syed, Adeel M.; Hassan, Taimur; Akram, M. Usman; Naz, Samra; Khalid, Shehzad
• Is Part Of: Computer methods and programs in biomedicine. Volume 137(2016)
• Page Start: 1
• Page End: 10
• Publication Date: 2016-12
• Subjects: Optical coherence tomography -- Medical image analysis -- Structure tensor -- Macular edema (ME) -- Central serous retinopathy (CSR) -- Retinal surfaces; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.09.004
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: An automated method is proposed here to diagnose two common macular syndromes by reconstructing 3D retinal surfaces. To the best of our knowledge, this paper proposes a first ever novel self-diagnosis system to incorporate 3D OCT scans. The self-diagnosis in our proposed system is based on multilevel support vector machines (SVM) classifier. 90 OCT volumes (30 healthy, 30 CSR and 30 ME) of 73 patients were considered in this research to test the proposed system. Abstract: Background and objectives: Macular diseases tend to damage macula within human retina due to which the central vision of a person is affected. Macular edema (ME) and central serous retinopathy (CSR) are two of the most common macular diseases. Many researchers worked on automated detection of ME from optical coherence tomography (OCT) and fundus images, whereas few researchers have worked on diagnosing central serous retinopathy. But this paper proposes a fully automated method for the classification of ME and CSR through robust reconstruction of 3D OCT retinal surfaces. Methods: The proposed system uses structure tensors to extract retinal layers from OCT images. The 3D retinal surface is then reconstructed by extracting the brightness scan (B-scan) thickness profile from each coherent tensor. The proposed system extracts 8 distinct features (3 based on retinal thickness profile of right side, 3 based on thickness profile of left side and 2 based on top surface and cyst spaces within retinal layers) from 30 labeled volumes (10 healthy, 10 CSR and 10 ME) which are used to train the supervised support vector machines (SVM) classifier. Results: In this research we have considered 90 OCT volumes (30 Healthy, 30 CSR and 30 ME) of 73 patients to test the proposed system where our proposed system correctly classified 89 out of 90 cases and has promising receiver operator characteristics (ROC) ratings with accuracy, sensitivity and specificity of 98.88%, 100%, and 96.66% respectively. Conclusion: The proposed system is quite fast and robust in detecting all the three types of retinal pathologies from volumetric OCT scans. The proposed system is fully automated and provides an early and on fly diagnosis of ME and CSR syndromes. 3D macular thickness surfaces can further be used as decision support parameter in clinical studies to check the volume of cyst.