東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Towards the automatization of crania...

AutoImplant Cranial Implant Design Challenge (2021 :)

FindBook

Google Book

Amazon

博客來

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 /

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 II/ edited 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:	AutoImplant Cranial Implant Design Challenge
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	ix, 129 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Skull - Congresses. - Surgery -
電子資源:	https://doi.org/10.1007/978-3-030-92652-6
ISBN:	9783030926526

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 /
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 ;13123..

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

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

3529660
Skull
--Surgery--Congresses.

LC Class. No.: RD529 / .T68 2021

Dewey Class. No.: 617.514

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 /
LDR:03071nmm 22003735a 4500 001 2258789
003 DE-He213
005 20211202223230.0
006 m d
007 cr nn 008maaau
008 220422s2021 sz s 0 eng d
020 $a 9783030926526 $q (electronic bk.)
020 $a 9783030926519 $q (paper)
024 7 $a 10.1007/978-3-030-92652-6 $2 doi
035 $a 978-3-030-92652-6
040 $a GP $c GP
041 0 $a eng
050 4 $a RD529 $b .T68 2021
072 7 $a UYQV $2 bicssc
072 7 $a COM016000 $2 bisacsh
072 7 $a UYQV $2 thema
082 0 4 $a 617.514 $2 23
090 $a RD529 $b .T737 2021
111 2 $a AutoImplant Cranial Implant Design Challenge $n (2nd : $d 2021 : $c Online) $3 3531589
245 1 0 $a Towards the automatization of cranial implant design in cranioplasty II $h [electronic resource] : $b second Challenge, AutoImplant 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021 : proceedings / $c edited by Jianning Li, Jan Egger.
246 3 $a AutoImplant 2021
246 3 $a MICCAI 2021
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a ix, 129 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 0302-9743 ; $v 13123
490 1 $a Image processing, computer vision, pattern recognition, and graphics
505 0 $a 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.
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.
650 0 $a Skull $x Surgery $v Congresses. $3 3529660
650 0 $a Implants, Artificial $x Design and construction $v Congresses. $3 3529661
650 1 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 890871
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Computer Applications. $3 891249
650 2 4 $a Computers and Education. $3 892757
700 1 $a Li, Jianning. $3 3529657
700 1 $a Egger, Jan. $3 3529658
710 2 $a SpringerLink (Online service) $3 836513
711 2 $a International Conference on Medical Image Computing and Computer-Assisted Intervention $n (24th : $d 2021 : $c Online) $3 3517961
773 0 $t Springer Nature eBook
830 0 $a Lecture notes in computer science ; $v 13123. $3 3531590
830 0 $a Image processing, computer vision, pattern recognition, and graphics. $3 3382509
856 4 0 $u https://doi.org/10.1007/978-3-030-92652-6
950 $a Computer Science (SpringerNature-11645)