Machine Learning Techniques for Ophthalmic Data Processing: A Review

Machine learning and especially deep learning techniques are dominating medical image and data analysis. This article reviews machine learning approaches proposed for diagnosing ophthalmic diseases during the last four years. Three diseases are addressed in this survey, namely diabetic retinopathy, age-related macular degeneration, and glaucoma. The review covers over 60 publications and 25 public datasets and challenges related to the detection, grading, and lesion segmentation of the three considered diseases. Each section provides a summary of the public datasets and challenges related to each pathology and the current methods that have been applied to the problem. Furthermore, the recent machine learning approaches used for retinal vessels segmentation, and methods of retinal layers and fluid segmentation are reviewed. Two main imaging modalities are considered in this survey, namely color fundus imaging, and optical coherence tomography. Machine learning approaches that use eye measurements and visual field data for glaucoma detection are also included in the survey. Finally, the authors provide their views, expectations and the limitations of the future of these techniques in the clinical practice.

Authors

• Mhd Hasan Sarhan
• M. Ali Nasseri
• Daniel Zapp
• Mathias Maier
• Chris P. Lohmann
• Nassir Navab
• Abouzar Eslami Translational Research Lab, Carl Zeiss Meditec AG, Munich, Germany

Keywords

• Image segmentation
• Diabetes
• Retinopathy
• Machine learning
• Lesions
• Retina
• Ophthalmology
• Glaucoma
• Image Analysis
• Public Datasets
• Recent Approaches
• Age-related Macular Degeneration
• Retinal Layer
• Retinal Vessels
• Fundus Images
• Medical Image Analysis
• Vessel Segmentation
• Lesion Segmentation
• Ophthalmic Diseases
• Retinal Segmentation
• Glaucoma Detection
• Color Fundus Images
• Neural Network
• Convolutional Neural Network
• Deep Neural Network
• Disease Detection
• Intersection Over Union
• Optical Coherence Tomography Scans
• Retinal Layer Segmentation
• Optical Coherence Tomography Images
• Optical Coherence Tomography Volume
• Retinal Nerve Fiber Layer
• Optic Cup
• Optic Nerve Head
• Geographic Atrophy
• Retinal Pigment Epithelium
• Ophthalmic diagnostics
• deep learning
• diabetic retinopathy
• Diagnostic Techniques, Ophthalmological
• Humans
• Image Interpretation, Computer-Assisted
• Retinal Diseases
• Tomography, Optical Coherence