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Towards the automatization of crania...

(1998 :)

Towards the automatization of cranial implant design in cranioplasty IIsecond Challenge, AutoImplant 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021 : proceedings /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Towards the automatization of cranial implant design in cranioplasty IIedited by Jianning Li, Jan Egger.
其他題名:	second Challenge, AutoImplant 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021 : proceedings /
其他題名:	AutoImplant 2021
其他作者:	Li, Jianning.
團體作者:
出版者:	Cham :Springer International Publishing :2021.
面頁冊數:	ix, 129 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	SkullCongresses.Surgery
電子資源:	https://doi.org/10.1007/978-3-030-92652-6
ISBN:	9783030926526$q(electronic bk.)

Towards the automatization of cranial implant design in cranioplasty IIsecond Challenge, AutoImplant 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021 : proceedings /
Towards the automatization of cranial implant design in cranioplasty IIsecond Challenge, AutoImplant 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021 : proceedings /[electronic resource] :AutoImplant 2021edited by Jianning Li, Jan Egger. - Cham :Springer International Publishing :2021. - ix, 129 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,131230302-9743 ;. - Lecture notes in computer science ;4891..

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.

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.

ISBN: 9783030926526$q(electronic bk.)

Standard No.: 10.1007/978-3-030-92652-6doiSubjects--Topical Terms:

884094
Skull
--Surgery--Congresses.

LC Class. No.: RD529 / .T68 2021

Dewey Class. No.: 617.514

Towards the automatization of cranial implant design in cranioplasty IIsecond Challenge, AutoImplant 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021 : proceedings /
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520 $a 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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