Computational Retinal Image Analysis: Tools, Applications and Perspectives

Front matter
Copyright
Contributors
A brief introduction and a glimpse into the past
Why this book?
Casting an eye into the distant past: The history of eye research in the West
Book structure
Acknowledgments
References
Clinical motivation and the needs for RIA in healthcare
Introduction
Assisting diagnosis of clinical eye diseases
Assessing severity and classifying clinical eye diseases
Capturing pre-clinical signs of the eye diseases
Identifying retinal changes associated with systemic diseases
Structural signs to functional signs
Perspectives—Precise diagnosis, replacing repetitive work, and exploring novel signs
References
The physics, instruments and modalities of retinal imaging
Introduction
Optics of the eye
Using the eye to record images of the retina
Spatial resolution of retinal images
Glare, contrast and image quality
How the physics of light propagation affects retinal image quality
Spectral characteristics of the eye
The use of eye phantoms to simulate retinal imaging
Ophthalmic instruments
Brief history
Safety exposure limits
The fundus camera
Indirect ophthalmoscopes
The scanning laser ophthalmoscopes
Handheld retinal cameras
Ultrawide field imaging
Optical coherence tomography
Time domain optical coherence tomography. The beauty of the en-face view
Spectral domain optical coherence tomography
Camera based optical coherence tomography and exceptional spatial resolutions
Swept source optical coherence tomography. Going faster and deeper into the tissue
Methods of generating images in SD-OCT
Modern topics in optical coherence tomography for eye imaging
Polarization and birefringence
Conclusions
References
Retinal image preprocessing, enhancement, and registration
Introduction
Intensity normalization
Fundus imaging
Tomographic imaging
Noise reduction and contrast enhancement
Fundus imaging
Tomographic imaging
Retinal image registration
Fundus imaging
Tomographic imaging
Intramodal vs. cross-modal image registration
Conclusions
Acknowledgment
References
Automatic landmark detection in fundus photography
Background
Optic disc
Macula lutea
Fovea and disc detection/segmentation—Utility
Retinal imaging databases
Algorithm accuracy
Optic disc and fovea detection
Automated localization of the optic disc, fovea, and retinal blood vessels from digital color fundus images (Sinthana ...
Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels (Hoover and Goldbaum, 2 ...
Detection of optic disc in retinal images by means of a geometrical model of vessel structure (Foracchia et al., 2004 ...
Fast localization and segmentation of the optic disc in retinal images using directional matched filtering and level ...
Multiscale sequential convolutional neural networks for simultaneous detection of the fovea and optic disc (Al-Bander ...
Summary
References
Retinal vascular analysis: Segmentation, tracing, and beyond
Introduction
Benchmark datasets and evaluation metrics
Datasets
Evaluation metrics
Vessel segmentation
Unsupervised segmentation
Supervised segmentation
Deep learning
Vessel tracing
Vascular junction identification
Vascular tree separation
Arterial/venous vessel classification
Clinical relevant vessel readouts
Summary and outlook
Vasculature analysis in emerging imaging techniques
Benchmarks and metrics
References
OCT layer segmentation
Anatomical description and clinical relevance
Algorithmic evaluation and benchmarking
Intensity based methods
Graph based methods
Deep learning based methods
Preprocessing and augmentation
Pixelwise semantic segmentation methods
Boundary detection methods
Discussion and conclusion
References
Image quality assessment
Introduction
Image quality of ophthalmic images
Applications of image quality assessment algorithms
Screening for diabetic retinopathy
Teleophthalmology and clinical decision making
Epidemiology study requirements
Automated image quality assessment algorithms
An overview of techniques
Datasets and metrics used to evaluate image quality
Examples of retinal image quality assessment systems
Algorithms based on generic image quality parameters
Information fusion
Algorithms based on structural image quality parameters
Image structure clustering
Segmentation map feature analysis
Algorithms based on deep learning
Convolutional neural networks
Human visual system information combined with convolutional neural networks
Conclusion
References
Validation
Introduction: Why is validation difficult?
Challenges
Annotations are expensive
Annotation tasks are often unfamiliar to clinicians
Consistency is hard to achieve
Collecting annotations may be limited by data governance
Image quality may vary across images and data sets
Absence of unambiguous ground truth
Time-varying quantities are not well represented by a single measurement
Test criteria and data sets are not uniform in the literature
Dependency on application/task
Human in the loop
Tools and techniques
Choosing images: Aligning data set with clinical criteria
Technical criteria
Clinical criteria
Direct techniques: Focus on the image processing task
Receiver operating characteristic (ROC) curves
Accuracy and related measures
Confusion matrices
Bland-Altman graphs
Cohen’s kappa and related measures
Error histograms
Eliminating outliers
Choosing an appropriate number of bins
Validation on outcome: Focus on the clinical task
Annotations and data, annotations as data
Annotation protocols and their importance
Reducing the need for manual annotations
Conclusion
Acknowledgments
References
Statistical analysis and design in ophthalmology: Toward optimizing your data
Introduction
Data analysis in ophthalmic and vision research
The contribution of statistics in ophthalmic and vision research
Data classification, data capture and data management
Data classification
Data collection and management
Words of caution about data collection in the current era of big data
Uncertainty and estimation
Uncertainty
The problem of estimation, P -values and confidence intervals
Words of caution on statistical and clinical significance and multiple tests
On choosing the right statistical analysis method
The most common statistical methods
How to decide what method to use?
Words of caution in the data analysis method selection
Missingness of data
Main mechanisms of data missingness
Main strategies to tackle missing data
Words of caution for dealing with missing data
Designing an ophthalmic study
Study designs, sample size calculation and power analysis
Words of caution for two eyes: What to do and what not to do?
Biomarkers
Ophthalmic imaging data challenges on intersection of statistics and machine learning
Discussion
References
Structure-preserving guided retinal image filtering for optic disc analysis
Introduction
Optic disc segmentation
Optic cup segmentation
Joint optic disc and optic cup segmentation
Image quality
Contributions
Structure-preserving guided retinal image filtering
Experimental results
Dataset
Evaluation metrics
Results
Application
Deep learning-based optic cup segmentation
Sparse learning-based CDR computation
Performance on regions with lesions
Conclusions
References
Diabetic retinopathy and maculopathy lesions
Introduction
The clinical impact of DR and maculopathy lesions
Type of lesions/clinical features
Lesion detection and segmentation
Morphology
Machine learning
Region growing
Thresholding
Deep learning
Miscellaneous
Performance comparison
Lesion localization
Conclusions
References
Drusen and macular degeneration
Introduction
Histopathological lesions and clinical classification
Normal aging of the macula
Lesions of non-neovascular AMD
Lesions of neovascular AMD
Automatic analysis of drusen and AMD-related pathologies
Drusen detection in retinal fundus photography
Characterization, classification and quantification of drusen
Machine learning based approaches
Drusen segmentation and measurement
Quantifying drusen area and distinguishing drusen type
Texture-based methods
Other imaging modalities
Angiography
Scanning laser ophthalmoscopy
Drusen detection in OCT
Analysis of other AMD lesions
Diagnosis of AMD
Datasets
Conclusions
References
OCT fluid detection and quantification
Introduction
Intraretinal cystoid fluid
Subretinal fluid
Sub-RPE fluid in PED
OCT fluid quantification
Segmentation using supervised learning
Preprocessing and postprocessing
Denoising
Retina and layer segmentation
Data augmentation
Traditional machine-learning and nonmachine-learning approaches
Segmentation using weakly supervised and unsupervised learning
Evaluation
OCT fluid detection
Detection using image segmentation
Detection using image classification
Traditional machine-learning approaches
Evaluation
Clinical applications
Structure function
Longitudinal analysis of VA outcomes
Method
Obtaining fluid volumes
Regression model
Experiments and results
Dataset
Regression model
Discussion and conclusions
Acknowledgments
References
Retinal biomarkers and cardiovascular disease: A clinical perspective
Introduction
The concept of retinal vascular imaging
Retinal vascular changes and heart disease
Retinal vascular changes and stroke
Clinical stroke
Subclinical stroke
Retinal vascular changes and CVD mortality
Clinical implications
Retinal vascular imaging as a tool to stratify CVD
Retinal imaging for clinical trials and outcome monitoring for CVD
New advances in retinal vascular imaging
Retinal imaging with artificial intelligence
Imaging of the choroidal vasculature
Imaging of the retinal capillary network
Ultra-widefield retinal imaging
Conclusions
References
Vascular biomarkers for diabetes and diabetic retinopathy screening
Introduction
The Sino-Dutch collaboration project RetinaCheck
Vascular analysis-specific biomarkers for early detection and screening
Layout of this chapter
Brain- and vision-inspired computing
The mathematics of V1: Sub-Riemannian geometry in SE (2)
Orientation scores
A moving frame of reference
Sub-Riemannian geometry
Application: Brain inspired image analysis
Preprocessing
Denoising in the SE (2) space
Vessel segmentation
Vessel completion
Validation studies
Vascular biomarkers
Vessel width
Vessel tortuosity
Single-vessel tortuosity
Global tortuosity
SE (2) tortuosity
Exponential curves in SE(2)
Fitting the best exponential curve in the orientation scores
Global tortuosity measurement via the exponential curvature
Bifurcations
Murray’s law
Bifurcation biomarkers
Fractal dimension
The processing pipeline
RHINO software and graphical user interface
Clinical validation studies
The Shengjing study
The Maastricht study
Discussion
References
Image analysis tools for assessment of atrophic macular diseases
The clinical need for automatic image analysis tools in retinal disease
Overview of analysis tools of atrophic AMD and risk factors for progression to atrophy
Semiautomated segmentation of atrophic macular diseases
Heidelberg RegionFinder for atrophic AMD segmentation in FAF images
Level set approach for atrophic AMD segmentation in OCT and FAF images
Automated segmentation of atrophic macular diseases
Supervised classification for atrophic AMD segmentation in FAF images using a traditional machine learning algorithm
Supervised classification for age-related and juvenile atrophic macular degeneration using an AI deep learning approa ...
Automated binary classification of OCT risk factors for progression from intermediate AMD to atrophy using an AI deep l ...
Summary
Acknowledgments
References
Artificial intelligence and deep learning in retinal image analysis
Introduction
Fundamentals of deep learning
Fundamentals of neural networks
Deep convolutional neural networks
CNNs for semantic image segmentation
Deep learning applications to retinal disease analysis
Deep learning for diabetic retinopathy
Deep learning for age-related macular degeneration
Deep learning for retinopathy of prematurity and glaucoma
Deep learning applications in OCT segmentation
Deep learning for retinal biomarker extraction
Automatic retinal biomarker discovery
Datasets
Conclusion
References
AI and retinal image analysis at Baidu
Baidu: Mission, products, and next-steps
The Baidu mission
AI in Baidu
Baidu Brain
Visual semantic AI
Speech semantic AI
Natural language AI
General architecture of AI retinal image analysis
Descriptive IQA
Focus and clarity assessment
Brightness and contrast assessment
Illumination evenness assessment
Disease-specific IQA
Discussion
Diabetic retinopathy detection algorithm
Preprocessing
Data augmentation
Classification model
Glaucoma detection algorithm
Age-related macular degeneration detection (AMD) algorithm
Macular AOI location
End-to-end referable AMD classifier
Drusen and neovascularization detector
Interpretation module
Experimental results and real-world application
Image quality assessment
Diabetic retinopathy
Glaucoma
Age-related macular degeneration
Real-world application
Outlook of Baidu retina system
Acknowledgments
References
The challenges of assembling, maintaining and making available large data sets of clinical data for research
Introduction
Sources of images and associated data
Research collected images
Routinely collected images
Sources of ground truth data
Linking clinical data to imaging data
Data governance
Key data protection terminology and concepts
Applications to access data for research
Controls
Safe data
Identifying information
Acceptance threshold for re-identification
Transformation of data
Considerations when anonymizing pixel data
Software to anonymize DICOM images
Safe people and organizations
Indexing and linking
Trusted third parties
Who will be accessing the research data
Safe access
Transferring data
Data hosted on a researcher managed environment
Safe Havens/trusted research environments
Federated or distributed analysis
Challenges of assembling large quantities of clinical data within data governance controls
Conclusions
References
Technical and clinical challenges of A.I. in retinal image analysis
Introduction
Progression of A.I. in retinal imaging
Technical challenges
Quantity of data
Quality of data
Heterogeneous data
Unbalanced data
Incomplete data
Private data
Model generalizability
Model interpretability
Model maintainability
Model deployability
Clinical challenges
Variation in DR classification systems and reference standards
Disagreement in clinical ground truth
Integration into clinical workflows
Privacy and data collection
Assignment of liability
Patient and physician acceptance of “black box” models
Expectation management
Conclusion
References
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