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 / edited by Jianning Li, Jan Egger.

Format:

Book

Contributor:

Li, Jianning, Editor.

Egger, Jan, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 12439

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12439

Language:

English

Subjects (All):

Image processing-Digital techniques.

Computer vision.

Artificial intelligence.

Social sciences-Data processing.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Artificial Intelligence.

Computer Application in Social and Behavioral Sciences.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Artificial Intelligence.

Computer Application in Social and Behavioral Sciences.

Physical Description:

1 online resource (XVI, 115 pages) : 76 illustrations, 72 illustrations in color.

Edition:

1st ed. 2020.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2020.

System Details:

text file PDF

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 a dataset descriptor in this volume were carefully reviewed and selected form numerous submissions. This challenge aims to provide more affordable, faster, and more patient-friendly solutions to the design and manufacturing of medical implants, including cranial implants, which is needed in order to repair a defective skull from a brain tumor surgery or trauma. The presented solutions can serve as a good benchmark for future publications regarding 3D volumetric shape learning and cranial implant design.

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.

Other Format:

Printed edition:

ISBN:

978-3-030-64327-0

9783030643270

Access Restriction:

Restricted for use by site license.