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Fetal, Infant and Ophthalmic Medical Image Analysis

Overview of attention for book
Cover of 'Fetal, Infant and Ophthalmic Medical Image Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 Template-Free Estimation of Intracranial Volume: A Preterm Birth Animal Model Study
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    Chapter 2 Assessing Reorganisation of Functional Connectivity in the Infant Brain
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    Chapter 3 Fetal Skull Segmentation in 3D Ultrasound via Structured Geodesic Random Forest
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    Chapter 4 Fast Registration of 3D Fetal Ultrasound Images Using Learned Corresponding Salient Points
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    Chapter 5 Automatic Segmentation of the Intracranial Volume in Fetal MR Images
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    Chapter 6 Abdomen Segmentation in 3D Fetal Ultrasound Using CNN-powered Deformable Models
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    Chapter 7 Multi-organ Detection in 3D Fetal Ultrasound with Machine Learning
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    Chapter 8 Robust Regression of Brain Maturation from 3D Fetal Neurosonography Using CRNs
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    Chapter 9 Segmentation of Retinal Blood Vessels Using Dictionary Learning Techniques
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    Chapter 10 Detecting Early Choroidal Changes Using Piecewise Rigid Image Registration and Eye-Shape Adherent Regularization
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    Chapter 11 Patch-Based Deep Convolutional Neural Network for Corneal Ulcer Area Segmentation
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    Chapter 12 Model-Driven 3-D Regularisation for Robust Segmentation of the Refractive Corneal Surfaces in Spiral OCT Scans
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    Chapter 13 Automatic Retinal Layer Segmentation Based on Live Wire for Central Serous Retinopathy
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    Chapter 14 Retinal Image Quality Classification Using Fine-Tuned CNN
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    Chapter 15 Optic Disc Detection via Deep Learning in Fundus Images
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    Chapter 16 3D Choroid Neovascularization Growth Prediction with Combined Hyperelastic Biomechanical Model and Reaction-Diffusion Model
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    Chapter 17 Retinal Biomarker Discovery for Dementia in an Elderly Diabetic Population
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    Chapter 18 Non-rigid Registration of Retinal OCT Images Using Conditional Correlation Ratio
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    Chapter 19 Joint Optic Disc and Cup Segmentation Using Fully Convolutional and Adversarial Networks
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    Chapter 20 Automated Segmentation of the Choroid in EDI-OCT Images with Retinal Pathology Using Convolution Neural Networks
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    Chapter 21 Spatiotemporal Analysis of Structural Changes of the Lamina Cribrosa
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    Chapter 22 Fast Blur Detection and Parametric Deconvolution of Retinal Fundus Images
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    Chapter 23 Towards Topological Correct Segmentation of Macular OCT from Cascaded FCNs
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    Chapter 24 Boosted Exudate Segmentation in Retinal Images Using Residual Nets
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    Chapter 25 Development of Clinically Based Corneal Nerves Tortuosity Indexes
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    Chapter 26 A Comparative Study Towards the Establishment of an Automatic Retinal Vessel Width Measurement Technique
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    Chapter 27 Automatic Detection of Folds and Wrinkles Due to Swelling of the Optic Disc
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    Chapter 28 Representation Learning for Retinal Vasculature Embeddings
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Title
Fetal, Infant and Ophthalmic Medical Image Analysis
Published by
Springer, Cham, January 2017
DOI 10.1007/978-3-319-67561-9
ISBNs
978-3-31-967560-2, 978-3-31-967561-9
Editors

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

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 58 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 24%
Student > Master 8 14%
Researcher 7 12%
Student > Bachelor 5 9%
Professor > Associate Professor 3 5%
Other 7 12%
Unknown 14 24%
Readers by discipline Count As %
Computer Science 19 33%
Engineering 16 28%
Medicine and Dentistry 2 3%
Energy 2 3%
Psychology 1 2%
Other 3 5%
Unknown 15 26%