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Towards the Automatization of Cranial Implant Design in Cranioplasty

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Cover of 'Towards the Automatization of Cranial Implant Design in Cranioplasty'

Table of Contents

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    Book Overview
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    Chapter 1 Patient Specific Implants (PSI)
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    Chapter 2 Dataset Descriptor for the AutoImplant Cranial Implant Design Challenge
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    Chapter 3 Automated Virtual Reconstruction of Large Skull Defects using Statistical Shape Models and Generative Adversarial Networks
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    Chapter 4 Cranial Implant Design Through Multiaxial Slice Inpainting Using Deep Learning
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    Chapter 5 Cranial Implant Design via Virtual Craniectomy with Shape Priors
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    Chapter 6 Deep Learning Using Augmentation via Registration: 1st Place Solution to the AutoImplant 2020 Challenge
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    Chapter 7 Cranial Defect Reconstruction Using Cascaded CNN with Alignment
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    Chapter 8 Shape Completion by U-Net: An Approach to the AutoImplant MICCAI Cranial Implant Design Challenge
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    Chapter 9 Cranial Implant Prediction Using Low-Resolution 3D Shape Completion and High-Resolution 2D Refinement
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    Chapter 10 Cranial Implant Design Using a Deep Learning Method with Anatomical Regularization
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    Chapter 11 High-Resolution Cranial Implant Prediction via Patch-Wise Training
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    Chapter 12 Learning Volumetric Shape Super-Resolution for Cranial Implant Design
Attention for Chapter 5: Cranial Implant Design via Virtual Craniectomy with Shape Priors
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Citations

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Summary Dimensions citations
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Chapter title
Cranial Implant Design via Virtual Craniectomy with Shape Priors
Chapter number 5
Book title
Towards the Automatization of Cranial Implant Design in Cranioplasty
Published by
Springer, Cham, December 2020
DOI 10.1007/978-3-030-64327-0_5
Book ISBNs
978-3-03-064326-3, 978-3-03-064327-0
Authors

Franco Matzkin, Virginia Newcombe, Ben Glocker, Enzo Ferrante, Matzkin, Franco, Newcombe, Virginia, Glocker, Ben, Ferrante, Enzo

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 50%
Student > Master 1 17%
Unknown 2 33%
Readers by discipline Count As %
Computer Science 1 17%
Engineering 1 17%
Unknown 4 67%

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