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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 10: Cranial Implant Design Using a Deep Learning Method with Anatomical Regularization
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Citations

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Chapter title
Cranial Implant Design Using a Deep Learning Method with Anatomical Regularization
Chapter number 10
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_10
Book ISBNs
978-3-03-064326-3, 978-3-03-064327-0
Authors

Bomin Wang, Zhi Liu, Yujun Li, Xiaoyan Xiao, Ranran Zhang, Yankun Cao, Lizhen Cui, Pengfei Zhang, Wang, Bomin, Liu, Zhi, Li, Yujun, Xiao, Xiaoyan, Zhang, Ranran, Cao, Yankun, Cui, Lizhen, Zhang, Pengfei

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 50%
Other 1 25%
Unknown 1 25%
Readers by discipline Count As %
Engineering 1 25%
Unknown 3 75%

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