Diabetes and Retinopathy

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Diabetes and Retinopathy
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Contributors
1
Complementary capabilities of photoacoustic imaging to existing optical ocular imaging techniques
	References
2
Intraretinal fluid map generation in optical coherence tomography images
	Introduction
	Optical coherence tomography: Background and significance
	The classical segmentation approach
	Fluid identification by means of a regional analysis
		ROI extraction
		Image sampling
		Classification
		Binary map creation
		Color map creation
	Discussion and conclusions
	Acknowledgments
	References
3
Fully automated identification and clinical classification of macular edema using optical coherence tomography ...
	Background and significance
	Computational identification and characterization of the MEs
		Region of interest delimitation
		Identification of the different types of macular edema
	Results and discussion
	Conclusions
	Acknowledgments
	References
4
Optimal surface segmentation with subvoxel accuracy in spectral domain optical coherence tomography images
	Introduction
	Methods
		Problem formulation and energy function
			Original formulation in regularly sampled space
			Formulation in irregularly sampled space to achieve subvoxel accuracy
		Graph construction
			Intracolumn edges
			Intercolumn edges
			Intersurface edges
		Surface recovery from minimum s-t cut
	Experimental methods
		Data
		Workflow
			Experiment for subvoxel accuracy
			Experiment for super-resolution accuracy
			Cost function design
			Gradient vector flow
			Parameter setting
	Results
		Results for subvoxel accuracy
		Results for super-resolution accuracy
	Discussion and conclusions
	References
5
Analysis of optical coherence tomography images using deep convolutional neural network for maculopathy grading
	Introduction
		Macular edema
		Age-related macular degeneration
		Central serous chorioretinopathy
	Retinal imaging modalities
		Optical coherence tomography
	Dataset description
	TU-Net: A deep CNN architecture for maculopathy grading
		Preprocessing
		Proposed TU-Net architecture
	Results and discussion
	Conclusion
	References
6
Segmentation of retinal layers from OCT scans
	Introduction
	Method
		Joint MGRF-based macula: Centred image segmentation
			Shape model Psp(m)
			Appearance model
		3D retinal layers segmentation
	Experimental results
	Conclusion
	References
7
Low-complexity computer-aided diagnosis for diabetic retinopathy
	Introduction
	Related work
	Low-complexity CNN for diabetic retinopathy
		Mathematical model and architecture
		Evaluation metrics
		Model hyperparameters
	Experimental results
	Discussion
	Conclusion
	References
8
Ophthalmic optical coherence tomography angiography in diabetes
	Introduction
	Qualitative and quantitative changes in ophthalmic blood vessels in diabetes detected by OCTA
		OCTA in patients with diabetes without clinically apparent DR
		OCTA in patients with nonproliferative DR
		OCTA in patients with proliferative DR
		OCTA in patients with diabetic macular edema
		Effect of various treatments of DR and DME to OCTA parameters
		OCTA at peripapillary area and optic nerve head in diabetes
	Relevance of findings of OCTA studies concerning pathophysiology of DR
	Conclusion
	References
9
Early detection of diabetics using retinal OCT images
	Introduction
	Traditional methods for early detection and assessment of diabetes
	Optical coherence tomography
	Early detection of diabetes using retinal OCT images
		Related work on early detection of diabetes using OCT images
		Proposed work
			Automatic segmentation of retinal OCT images
			Feature extraction
			Classification of diabetes using an RF classifier
		Experimental results
	Discussion and conclusions
	Acknowledgment
	References
10
A noninvasive approach for the early detection of diabetic retinopathy
	Introduction
	Methods
	Results
	Validation
	Conclusion
	References
Index