Li / Egger | Towards the Automatization of Cranial Implant Design in Cranioplasty II | E-Book | sack.de
E-Book

E-Book, Englisch, 129 Seiten, eBook

Reihe: Image Processing, Computer Vision, Pattern Recognition, and Graphics

Li / Egger Towards the Automatization of Cranial Implant Design in Cranioplasty II

Second Challenge, AutoImplant 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings
1. Auflage 2021
ISBN: 978-3-030-92652-6
Verlag: Springer International Publishing
Format: PDF
 Kopierschutz: 1 - PDF Watermark

Second Challenge, AutoImplant 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings

E-Book, Englisch, 129 Seiten, eBook

Reihe: Image Processing, Computer Vision, Pattern Recognition, and Graphics

ISBN: 978-3-030-92652-6
Verlag: Springer International Publishing
Format: PDF
 Kopierschutz: 1 - PDF Watermark

This book constitutes the Second Automatization of Cranial Implant Design in Cranioplasty Challenge, AutoImplant 2021, which was held in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, in Strasbourg, France, in September, 2021. The challenge took place virtually due to the COVID-19 pandemic.The 7 papers are presented together with one invited paper, one qualitative evaluation criteria from neurosurgeons and a dataset descriptor. 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.
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Zielgruppe

Research

Autoren/Hrsg.

Li, Jianning
Egger, Jan

Weitere Infos & Material

Personalized Calvarial Reconstruction in Neurosurgery.- Qualitative Criteria for Designing Feasible Cranial Implants.- Segmentation of Defective Skulls from CT Data for Tissue Modelling.- Improving the Automatic Cranial Implant Design in Cranioplasty by Linking Different Datasets.- Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation.- A U-Net based System for Cranial Implant Design with Pre-processing and Learned Implant Filtering.- Sparse Convolutional Neural Network for Skull Reconstruction.- Cranial Implant Prediction by Learning an Ensemble of Slice-based Skull Completion networks.- PCA-Skull: 3D Skull Shape Modelling Using Principal Component Analysis.- Cranial Implant Design using V-Net based Region of Interest Reconstruction.

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