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Medical Image Understanding and Analysis

Overview of attention for book
Cover of 'Medical Image Understanding and Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 Textural Feature Based Segmentation: A Repeatable and Accurate Segmentation Approach for Tumors in PET Images
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    Chapter 2 Unlearning Scanner Bias for MRI Harmonisation in Medical Image Segmentation
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    Chapter 3 Semantic Segmentation of Histopathological Slides for the Classification of Cutaneous Lymphoma and Eczema
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    Chapter 4 Autofocus Net: Auto-focused 3D CNN for Brain Tumour Segmentation
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    Chapter 5 Cortical Plate Segmentation Using CNNs in 3D Fetal Ultrasound
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    Chapter 6 Improving U-Net Segmentation with Active Contour Based Label Correction
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    Chapter 7 Segmenting Hepatocellular Carcinoma in Multi-phase CT
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    Chapter 8 On New Convolutional Neural Network Based Algorithms for Selective Segmentation of Images
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    Chapter 9 Segmentation of the Biliary Tree from MRCP Images via the Monogenic Signal
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    Chapter 10 Transfer Learning for Brain Segmentation: Pre-task Selection and Data Limitations
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    Chapter 11 Pancreas Segmentation-Derived Biomarkers: Volume and Shape Metrics in the UK Biobank Imaging Study
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    Chapter 12 Localization and Identification of Lumbar Intervertebral Discs on Spine MR Images with Faster RCNN Based Shortest Path Algorithm
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    Chapter 13 DeepSplit: Segmentation of Microscopy Images Using Multi-task Convolutional Networks
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    Chapter 14 A Framework for Jointly Assessing and Reducing Imaging Artefacts Automatically Using Texture Analysis and Total Variation Optimisation for Improving Perivascular Spaces Quantification in Brain Magnetic Resonance Imaging
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    Chapter 15 Groupwise Multimodal Image Registration Using Joint Total Variation
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    Chapter 16 CT Scan Registration with 3D Dense Motion Field Estimation Using LSGAN
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    Chapter 17 A Supervised Image Registration Approach for Late Gadolinium Enhanced MRI and Cine Cardiac MRI Using Convolutional Neural Networks
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    Chapter 18 Unsupervised Deep Learning for Stain Separation and Artifact Detection in Histopathology Images
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    Chapter 19 Asymmetric Point Spread Function Estimation and Deconvolution for Serial-Sectioning Block-Face Imaging
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    Chapter 20 Automatic and Objective Facial Palsy Grading Index Prediction Using Deep Feature Regression
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    Chapter 21 Prediction of Thrombectomy Functional Outcomes Using Multimodal Data
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    Chapter 22 Radiomics: A New Biomedical Workflow to Create a Predictive Model
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    Chapter 23 Going Deeper into Cardiac Motion Analysis to Model Fine Spatio-Temporal Features
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    Chapter 24 Ridge Detection and Analysis of Susceptibility-Weighted Magnetic Resonance Imaging in Neonatal Hypoxic-Ischaemic Encephalopathy
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    Chapter 25 A Machine Learning Approach for Colles’ Fracture Treatment Diagnosis
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    Chapter 26 A Lightweight CNN and Joint Shape-Joint Space ($$JS^2$$) Descriptor for Radiological Osteoarthritis Detection
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    Chapter 27 Discovering Unknown Diseases with Explainable Automated Medical Imaging
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    Chapter 28 Investigating Sex Related Phenotype Changes in Knockout Mice by Applying Deep Learning to X-Ray Images
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    Chapter 29 A Deep Learning Approach for Semantic Segmentation of Gonioscopic Images to Support Glaucoma Categorization
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    Chapter 30 A Deep Learning Approach to Detect the Demarcation Line in OCT Images
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    Chapter 31 Retinal Biomarkers Discovery for Cerebral Small Vessel Disease in an Older Population
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    Chapter 32 Simultaneous Optimisation of Confocal and Non-confocal Images in an AOSLO with a Reconfigurable Aperture Pattern
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    Chapter 33 Deep Generative Models to Simulate 2D Patient-Specific Ultrasound Images in Real Time
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    Chapter 34 Volumetric Simulation of Nano-Fibres and 2D SEM and 3D XCT Imaging Processes
Attention for Chapter 4: Autofocus Net: Auto-focused 3D CNN for Brain Tumour Segmentation
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Chapter title
Autofocus Net: Auto-focused 3D CNN for Brain Tumour Segmentation
Chapter number 4
Book title
Medical Image Understanding and Analysis
Published by
Springer, Cham, July 2020
DOI 10.1007/978-3-030-52791-4_4
Book ISBNs
978-3-03-052790-7, 978-3-03-052791-4
Authors

Andreas Stefani, Roushanak Rahmat, David Harris-Birtill

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 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 67%
Student > Bachelor 1 33%
Readers by discipline Count As %
Computer Science 3 100%