Ophthalmic Medical Image Analysis: 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings 9783030634186, 9783030634193

Table of contents :
Preface
Organization
Contents
Bio-inspired Attentive Segmentation of Retinal OCT Imaging
1 Introduction
2 Methods
2.1 Bio-inspired Attentive Segmentation
2.2 Low-Rank Oriented Attention (LROA)
2.3 Architectural Overview
3 Experiments and Results
3.1 Data
3.2 Experimental Setup
3.3 Results
4 Discussion and Conclusion
References
DR Detection Using Optical Coherence Tomography Angiography (OCTA):pg A Transfer Learning Approach with Robustness Analysis
1 Introduction
2 Methods
2.1 Datasets and Imaging Devices
2.2 Data Augmentation and Transfer Learning
3 Results
3.1 Classification of Controls, DR and NoDR Patients
3.2 Model Validation on OCTAGON Dataset
4 Discussion and Conclusions
References
What is the Optimal Attribution Method for Explainable Ophthalmic Disease Classification?
1 Introduction
2 Related Studies
3 Methods
4 Analysis
4.1 Quantitative Analysis
4.2 Qualitative Analysis
5 Conclusion
References
DeSupGAN: Multi-scale Feature Averaging Generative Adversarial Network for Simultaneous De-blurring and Super-Resolution of Retinal Fundus Images
1 Introduction
2 Related Work
3 Methodology
3.1 DeSupGAN Structure
3.2 Loss Functions
4 Experiment
4.1 Dataset Generation
4.2 Training Details
4.3 Results
4.4 Ablation Studies
5 Conclusion
References
Encoder-Decoder Networks for Retinal Vessel Segmentation Using Large Multi-scale Patches
1 Introduction
2 Studying Patch Size and Model Architecture
2.1 Effective Patch Sizes
2.2 Efficient Architecture
3 Comparison with State-of-the-Art
3.1 Results
3.2 High-Resolution Fundus Images
3.3 Cross-Dataset Evaluation
4 Conclusion
References
Retinal Image Quality Assessment via Specific Structures Segmentation
1 Introduction
2 Database
3 Method
3.1 Segmentation Modules
3.2 Quality Assessment Module
3.3 Implementation Detail
4 Results
4.1 Comparative Studies
4.2 Ablation Studies
4.3 Computational Complexity
5 Conclusion
References
Cascaded Attention Guided Network for Retinal Vessel Segmentation
1 Introduction
2 Methodology
2.1 Cascaded Deep Learning Network
2.2 Attention UNet++
3 Experiments
3.1 Datasets
3.2 Implementation Details
3.3 Evaluation Methods
3.4 Results
3.5 Ablation Study
4 Conclusion
References
Self-supervised Denoising via Diffeomorphic Template Estimation: Application to Optical Coherence Tomography
1 Introduction
2 Methods
2.1 Problem Formulation
2.2 Registration
2.3 Denoising
3 Experiments
3.1 Dataset
3.2 Implementation Details
3.3 Evaluation Methods
3.4 Results
4 Discussion
References
Automated Detection of Diabetic Retinopathy from Smartphone Fundus Videos
1 Introduction
2 Materials and Methods
2.1 Data Acquisition and Annotation
2.2 Cropping Frames to the Lens
2.3 Selection of Informative Frames
2.4 Classification of Referable Diabetic Retinopathy
3 Results
3.1 Evaluation of Informative Frame Selection
3.2 Evaluation of Disease Detection
3.3 Computational Effort
4 Discussion and Conclusion
References
Optic Disc, Cup and Fovea Detection from Retinal Images Using U-Net++ with EfficientNet Encoder
1 Introduction
2 Methodology
2.1 Dataset
2.2 Proposed Method
3 Results and Discussion
3.1 Experimental Set-up
3.2 Results and Discussion
4 Conclusion
References
Multi-level Light U-Net and Atrous Spatial Pyramid Pooling for Optic Disc Segmentation on Fundus Image
1 Introduction
2 Methodology
2.1 Light U-Net
2.2 Atrous Convolution and Spatial Pyramid Pooling
3 Experiments
3.1 Dataset and Evaluation Criteria
3.2 Implementation Details
3.3 Comparison with State-of-the-Art
3.4 Ablation Study
4 Conclusions
References
An Interactive Approach to Region of Interest Selection in Cytologic Analysis of Uveal Melanoma Based on Unsupervised Clustering
1 Introduction
2 Method
2.1 Step-1 Clustering
2.2 Step-2 Clustering
2.3 Interactive Centroid Assignment and Refinement
3 Experiment
3.1 Experiment Setup
3.2 Ablation Study for Clustering Algorithm
3.3 Ablation Study for Interactive Refinement
4 Conclusion
References
Retinal OCT Denoising with Pseudo-Multimodal Fusion Network
1 Introduction
2 Methods
3 Experiments
3.1 Data Set
3.2 Experimental Design
4 Results
4.1 Visual Analysis
4.2 Quantitative Evaluation
5 Conclusion and Future Work
References
Deep-Learning-Based Estimation of 3D Optic-Nerve-Head Shape from 2D Color Fundus Photographs in Cases of Optic Disc Swelling
1 Introduction
2 Methods
2.1 Overview
2.2 Neural Network Architecture
2.3 Statistical Total Retinal Shape Models in 3D
3 Experimental Methods
4 Results
5 Discussion and Conclusion
References
Weakly Supervised Retinal Detachment Segmentation Using Deep Feature Propagation Learning in SD-OCT Images
1 Introduction
2 Methodology
2.1 Saliency Map Generation Based on Improved CAM
2.2 Soft Label Using Feature Propagation Learning
2.3 Lesion Segmentation with Strong Supervised Network
3 Experiments
3.1 Datasets and Evaluation Metrics
3.2 Comparison Experiments
4 Conclusion
References
A Framework for the Discovery of Retinal Biomarkers in Optical Coherence Tomography Angiography (OCTA)
1 Introduction
2 Methods
2.1 Vascular Graph Construction
2.2 Graph Simplification
2.3 Feature Extraction
2.4 Demographics and Statistical Analysis
3 Results
4 Discussion and Conclusions
References
An Automated Aggressive Posterior Retinopathy of Prematurity Diagnosis System by Squeeze and Excitation Hierarchical Bilinear Pooling Network
1 Introduction
2 Methodology
2.1 Hierarchical Bilinear Pooling Module
2.2 Squeeze and Excitation Module
2.3 Focal-Loss
3 Experiments
3.1 Data
3.2 Results
4 Conclusions
References
Weakly-Supervised Lesion-Aware and Consistency Regularization for Retinitis Pigmentosa Detection from Ultra-Widefield Images
1 Introduction
2 Methodology
2.1 Multi-scale Global Average Pooling
2.2 Consistency Regularization
3 Experiments
3.1 Dataset and Implementation
3.2 Lesion Attention Map Visualization
3.3 Classification Performance
4 Conclusion
References
A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement
1 Introduction
2 Pix2Pix-Fundus Oculi Quality Enhancer (P2P-FOQE)
2.1 Pre-enhancement
2.2 Pix2Pix Enhancement
2.3 Post-enhancement
2.4 Training the P2P-FOQE Model
3 Experimental Evaluation
3.1 Dataset for Eye Fundus Image Quality Enhancement
3.2 Experimental Setup
3.3 Results and Discussion
4 Conclusions
References
Construction of Quantitative Indexes for Cataract Surgery Evaluation Based on Deep Learning
1 Introduction
2 Proposed Method
2.1 ResUnet for Pupil Segmentation
2.2 Pretrained ResNet for Keratome Localization
2.3 Constructing the Evaluation Indexes of Incision
3 Experiment Results
3.1 Dataset
3.2 Model Settings
3.3 Result and Discussion
4 Conclusion
References
Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification
1 Introduction
2 Related Work
3 Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis (DLGP-DR)
3.1 Feature Extraction - Inception-V3
3.2 Gaussian Processes
4 Experimental Evaluation
4.1 Datasets
4.2 Experimental Setup
4.3 EyePACS Results
4.4 Messidor-2 Results
4.5 Discussion
5 Conclusions
References
Author Index