Towards the automatization of cranial implant design in cranioplasty : first challenge, AutoImplant 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, proceedings (eBook, 2020) [WorldCat.org]
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Towards the automatization of cranial implant design in cranioplasty : first challenge, AutoImplant 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, proceedings
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Towards the automatization of cranial implant design in cranioplasty : first challenge, AutoImplant 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, proceedings

Author: Jianning Li; Jan Egger
Publisher: Cham, Switzerland : Springer, [2020]
Series: Lecture notes in computer science, 12439.; LNCS sublibrary., SL 6,, Image processing, computer vision, pattern recognition, and graphics.
Edition/Format:   eBook : Document : Conference publication : EnglishView all editions and formats
Summary:
This book constitutes the First Automatization of Cranial Implant Design in Cranioplasty Challenge, AutoImplant 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The challenge took place virtually due to the COVID-19 pandemic. The 10 papers presented together with one invited paper and  Read more...
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Genre/Form: Electronic books
Congress
Conference papers and proceedings
Actes de congrès
Congresses
Congrès
Additional Physical Format: Print version:
Towards the automatization of cranial implant design in cranioplasty.
Cham, Switzerland : Springer, [2020]
(OCoLC)1202056437
Material Type: Conference publication, Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Jianning Li; Jan Egger
ISBN: 9783030643270 3030643271
OCLC Number: 1225888917
Notes: Includes author index.
Description: 1 online resource (xvi, 115 pages) : illustrations (some color)
Contents: Patient Specific Implants (PSI): Cranioplasty in the Neurosurgical Clinical Routine --
Dataset Descriptor for the AutoImplant Cranial Implant Design Challenge --
Automated Virtual Reconstruction of Large Skull Defects using Statistical Shape Models and Generative Adversarial Networks --
Cranial Implant Design through Multiaxial Slice Inpainting using Deep Learning --
Cranial Implant Design via Virtual Craniectomy with Shape Priors --
Deep Learning Using Augmentation via Registration: 1st Place Solution to the AutoImplant 2020 Challenge --
Cranial Defect Reconstruction using Cascaded CNN with Alignment --
Shape Completion by U-Net: An Approach to the AutoImplant MICCAI Cranial Implant Design Challenge --
Cranial Implant Prediction using Low-Resolution 3D Shape Completion and High-Resolution 2D Refinement --
Cranial Implant Design Using a Deep Learning Method with Anatomical Regularization --
High-resolution Cranial Implant Prediction via Patch-wise Training --
Learning Volumetric Shape Super-Resolution for Cranial Implant Design.
Series Title: Lecture notes in computer science, 12439.; LNCS sublibrary., SL 6,, Image processing, computer vision, pattern recognition, and graphics.
Responsibility: Jianning Li, Jan Egger (eds.).

Abstract:

This book constitutes the First Automatization of Cranial Implant Design in Cranioplasty Challenge, AutoImplant 2020, which was held in conjunction with the 23rd International Conference on Medical  Read more...

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