Fetal, Infant and Ophthalmic Medical Image Analysis

Name: Fetal, Infant and Ophthalmic Medical Image Analysis | International Workshop, FIFI 2017, and 4th International Workshop, OMIA 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings
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International Workshop, FIFI 2017, and 4th International Workshop, OMIA 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings

M. Jorge Cardoso Tal Arbel Andrew Melbourne Hrvoje Bogunovic Pim Moeskops Xinjian Chen Ernst Schwartz Mona Garvin Emma Robinson Emanuele Trucco Michael Ebner Yanwu Xu Antonios Makropoulos Adrien Desjardin Tom Vercauteren(Herausgeber*in)

Springer (Verlag)

Erschienen am 6. September 2017

XIII, 252 Seiten

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978-3-319-67561-9 (ISBN)

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Inhalt

Intro
Workshop Editors
Preface FIFI 2017
Organization
Preface OMIA 2017
Organization
Contents
International Workshop on Fetal and Infant Image Analysis, FIFI 2017
Template-Free Estimation of Intracranial Volume: A Preterm Birth Animal Model Study
1 Introduction
2 Methods
2.1 Probabilistic Framework
2.2 Bayesian Inference
3 Experiments and Results
3.1 Data
3.2 Experiments on Rabbit Dataset
3.3 Experiments on Human Dataset
4 Discussion and Conclusion
References
Assessing Reorganisation of Functional Connectivity in the Infant Brain
1 Introduction
2 Methodology
3 Results
4 Conclusion
References
Fetal Skull Segmentation in 3D Ultrasound via Structured Geodesic Random Forest
1 Introduction
2 Method
2.1 Geodesic Contextual Information
2.2 Structured Label Space
2.3 Contextual Feature Space
3 Results and Discussion
4 Conclusions
References
Fast Registration of 3D Fetal Ultrasound Images Using Learned Corresponding Salient Points
1 Introduction
1.1 Related Work
2 Method
2.1 Image Registration Framework
2.2 Extraction of Candidate Points
2.3 SVM Saliency Detector
2.4 Generation of Training Data
3 Materials and Experiments
4 Results
5 Discussion and Conclusions
References
Automatic Segmentation of the Intracranial Volume in Fetal MR Images
1 Introduction
2 Data
3 Method
4 Experiments and Results
5 Discussion
References
Abdomen Segmentation in 3D Fetal Ultrasound Using CNN-powered Deformable Models
1 Introduction
2 Methods
2.1 Model-Based Segmentation
2.2 Model Initialization
2.3 Fovea Fully Convolutional Networks (FovFCN)
2.4 FovFCN-Powered Model-Based Segmentation
3 Experiments and Results
3.1 Image Data
3.2 Intensity-Only MBS
3.3 FovFCN
3.4 FovFCN-Powered MBS
4 Discussion and Conclusion
References
Multi-organ Detection in 3D Fetal Ultrasound with Machine Learning
1 Introduction
2 Method
2.1 Fetus Reference Coordinate System Definition
2.2 Organ Detection
3 Results and Discussion
3.1 Material and Experimental Setup
3.2 Fetus Reference Coordinate System Validation
3.3 Landmarks/Organs Detection Validation
4 Conclusion
References
Robust Regression of Brain Maturation from 3D Fetal Neurosonography Using CRNs
1 Introduction
2 Methods
2.1 Image Pre-processing
2.2 CRN Architectures
2.3 Loss Functions for Robust Maturation Regression
3 Experiments
4 Results and Discussion
5 Conclusion
References
4th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2017
Segmentation of Retinal Blood Vessels Using Dictionary Learning Techniques
1 Introduction
2 Related Work
3 RBVS Using Dictionary Learning
3.1 Sparse Coding and Dictionary Learning
3.2 RBVS by Sparse Coding Then Classifier (SCTC)
3.3 RBVS by Joint Dictionary and Classifier Learning (JDCL)
3.4 RBVS by One Dictionary per Class (DPC)
3.5 Post-processing: Total Variation with 1 Fidelity Norm (TV-1)
4 Experiments
4.1 Data Preparation
4.2 Experimental Setup and Measurements
4.3 Results and Discussions
5 Summary and Perspectives
References
Detecting Early Choroidal Changes Using Piecewise Rigid Image Registration and Eye-Shape Adherent Regularization
1 Introduction
2 Material and Method
3 Results and Discussion
4 Conclusion
References
Patch-Based Deep Convolutional Neural Network for Corneal Ulcer Area Segmentation
Abstract
1 Introduction
2 Materials and Methods
2.1 Dataset and Preprocessing
2.2 Deep Neural Network
2.3 Training
2.4 Implementation Details
3 Results
3.1 Baseline Method
3.2 VGG Net
4 Conclusion
Acknowledgments
References
Model-Driven 3-D Regularisation for Robust Segmentation of the Refractive Corneal Surfaces in Spiral OCT Scans
1 Introduction
2 Methods
2.1 Graph-Based Segmentation
2.2 Regularisation
2.3 Evaluation
3 Results
4 Discussion
References
Automatic Retinal Layer Segmentation Based on Live Wire for Central Serous Retinopathy
1 Introduction
2 Initial Surface Detection via Random Forest
3 Surface Refinement via Live Wire
4 Experiments
4.1 Data
4.2 Evaluation
4.3 Results
5 Conclusion
References
Retinal Image Quality Classification Using Fine-Tuned CNN
Abstract
1 Introduction
2 Method
2.1 Image Preprocessing
2.2 Data Augmentation
2.3 Network Architecture
2.4 Training
3 Experimental Result
4 Conclusions
References
Optic Disc Detection via Deep Learning in Fundus Images
Abstract
1 Introduction
2 Proposed Method
2.1 Preprocessing
2.2 Feature Representation by CNN
3 Experiments and Results
3.1 Databases
3.2 Results and Discussions
4 Conclusion
References
3D Choroid Neovascularization Growth Prediction with Combined Hyperelastic Biomechanical Model and R ...
Abstract
1 Introduction
2 Methods
2.1 Pre-processing
2.2 CNV Growth Modeling
2.3 Genetic Algorithm for Parameter Optimization
2.4 Prediction
3 Experimental Results
4 Conclusion and Discussion
References
Retinal Biomarker Discovery for Dementia in an Elderly Diabetic Population
Abstract
1 Introduction
2 Materials and Methods
2.1 Dataset
2.2 Feature Extraction
2.3 Biomarker Discovery by Regularized Logistic Regression
3 Results
4 Discussion and Conclusions
Acknowledgement
References
Non-rigid Registration of Retinal OCT Images Using Conditional Correlation Ratio
Abstract
1 Introduction
2 Methodology
2.1 The Similarity Measure Metrics of Conditional Correlation Ratio (CCR)
2.2 Derivatives and Optimization
3 Experimental Results
4 Conclusion
Acknowledgement
References
Joint Optic Disc and Cup Segmentation Using Fully Convolutional and Adversarial Networks
1 Introduction
2 Methods
2.1 Proposed Base Network Architecture (ResU-net)
2.2 Adversarial Network
3 Experiments and Results
4 Conclusion
References
Automated Segmentation of the Choroid in EDI-OCT Images with Retinal Pathology Using Convolution Neural Networks
1 Introduction
2 Method
2.1 Generating Edge Probability Maps Using CNN
2.2 Seam Carving
3 Evaluation and Results
3.1 Data
3.2 Results
4 Discussion
5 Conclusion
References
Spatiotemporal Analysis of Structural Changes of the Lamina Cribrosa
1 Introduction
2 Methods
2.1 Digital Filtering
2.2 Average Template Construction
2.3 Segmentation
2.4 Spatiotemporal Analysis
3 Experimental Results
4 Discussion
References
Fast Blur Detection and Parametric Deconvolution of Retinal Fundus Images
1 Introduction
2 Method
2.1 Classification Using Convolutional Neural Networks
2.2 Non-blind Image Deconvolution
3 Experimental Results
4 Conclusion
References
Towards Topological Correct Segmentation of Macular OCT from Cascaded FCNs
1 Introduction
2 Method
2.1 Preprocessing
2.2 Segmentation Network Architecture
2.3 Topology Correction Network Architecture
2.4 Topology Checking
2.5 Training
3 Experiments
3.1 Data
3.2 Comparison to Manual Segmentation
3.3 Evaluation of Topology Correction
4 Conclusions
References
Boosted Exudate Segmentation in Retinal Images Using Residual Nets
1 Introduction
2 Methodology
2.1 Material
2.2 Network Details
2.3 Importance Sampling
3 Experiments
4 Conclusion
References
Development of Clinically Based Corneal Nerves Tortuosity Indexes
Abstract
1 Introduction
2 Material
2.1 Ground Truth Dataset for Tortuosity Indexes Definition
2.2 Dataset for Investigation on Healthy and Diabetic Individuals
3 Methods
3.1 Corneal Nerve Tracing
3.2 Nerve Tortuosity Metrics
3.3 Normalization
3.4 Aggregation
3.5 Combination of Tortuosity Metrics
4 Results
4.1 Evaluation of Short- and Long-Range Tortuosity Indexes
4.2 Evaluation on Healthy and Diabetic Individuals
5 Discussion and Conclusions
References
A Comparative Study Towards the Establishment of an Automatic Retinal Vessel Width Measurement Technique
1 Introduction
2 Methodology
3 Experimental Result
3.1 Study Population
3.2 Vessel Width Measurement Tools
3.3 Results
4 Conclusion
References
Automatic Detection of Folds and Wrinkles Due to Swelling of the Optic Disc
1 Introduction
2 Methods
2.1 Image Generation
2.2 Feature Extraction
2.3 Classification
3 Experimental Methods
4 Results
5 Discussion and Conclusion
References
Representation Learning for Retinal Vasculature Embeddings
1 Introduction
2 Methods
3 Experiments and Results
4 Conclusions
References
Author Index

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