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Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis

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Cover of 'Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 DeepCS: Deep Convolutional Neural Network and SVM Based Single Image Super-Resolution
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    Chapter 2 Automatic Segmentation of Thigh Muscle in Longitudinal 3D T1-Weighted Magnetic Resonance (MR) Images
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    Chapter 3 Detecting Bone Lesions in Multiple Myeloma Patients Using Transfer Learning
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    Chapter 4 Quantification of Local Metabolic Tumor Volume Changes by Registering Blended PET-CT Images for Prediction of Pathologic Tumor Response
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    Chapter 5 Optimizing External Surface Sensor Locations for Respiratory Tumor Motion Prediction
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    Chapter 6 Segmentation of Fetal Adipose Tissue Using Efficient CNNs for Portable Ultrasound
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    Chapter 7 Automatic Shadow Detection in 2D Ultrasound Images
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    Chapter 8 Multi-channel Groupwise Registration to Construct an Ultrasound-Specific Fetal Brain Atlas
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    Chapter 9 Investigating Brain Age Deviation in Preterm Infants: A Deep Learning Approach
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    Chapter 10 Segmentation of Pelvic Vessels in Pediatric MRI Using a Patch-Based Deep Learning Approach
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    Chapter 11 Multi-view Image Reconstruction: Application to Fetal Ultrasound Compounding
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    Chapter 12 EchoFusion: Tracking and Reconstruction of Objects in 4D Freehand Ultrasound Imaging Without External Trackers
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    Chapter 13 Better Feature Matching for Placental Panorama Construction
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    Chapter 14 Combining Deep Learning and Multi-atlas Label Fusion for Automated Placenta Segmentation from 3DUS
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    Chapter 15 LSTM Spatial Co-transformer Networks for Registration of 3D Fetal US and MR Brain Images
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    Chapter 16 Automatic and Efficient Standard Plane Recognition in Fetal Ultrasound Images via Multi-scale Dense Networks
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    Chapter 17 Paediatric Liver Segmentation for Low-Contrast CT Images
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Title
Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis
Published by
Springer International Publishing, January 2018
DOI 10.1007/978-3-030-00807-9
ISBNs
978-3-03-000806-2, 978-3-03-000807-9
Editors

Andrew Melbourne, Roxane Licandro, Matthew DiFranco, Paolo Rota, Melanie Gau, Martin Kampel, Rosalind Aughwane, Pim Moeskops, Ernst Schwartz, Emma Robinson, Antonios Makropoulos

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The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 2 22%
Student > Bachelor 1 11%
Student > Ph. D. Student 1 11%
Unknown 5 56%
Readers by discipline Count As %
Computer Science 3 33%
Biochemistry, Genetics and Molecular Biology 1 11%
Unknown 5 56%