國立虎尾科技大學 |

Cerebral Aneurysm Detection and Analysis = First Challenge, CADA 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Cerebral Aneurysm Detection and Analysis/ edited by Anja Hennemuth, Leonid Goubergrits, Matthias Ivantsits, Jan-Martin Kuhnigk.
其他題名:	First Challenge, CADA 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings /
其他作者:	Hennemuth, Anja.
面頁冊數:	X, 113 p. 47 illus., 41 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Optical data processing. -
電子資源:	https://doi.org/10.1007/978-3-030-72862-5
ISBN:	9783030728625

Cerebral Aneurysm Detection and Analysis = First Challenge, CADA 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings /
Cerebral Aneurysm Detection and AnalysisFirst Challenge, CADA 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings /[electronic resource] :edited by Anja Hennemuth, Leonid Goubergrits, Matthias Ivantsits, Jan-Martin Kuhnigk. - 1st ed. 2021. - X, 113 p. 47 illus., 41 illus. in color.online resource. - Image Processing, Computer Vision, Pattern Recognition, and Graphics ;12643. - Image Processing, Computer Vision, Pattern Recognition, and Graphics ;9219.

Overview of the CADA Challenge at MICCAI 2020 -- Cerebral Aneurysm Detection and Analysis Challenge 2020 (CADA) -- Introduction -- CADA: Clinical Background and Motivation -- Cerebral Aneurysm Detection -- Deep Learning-Based 3D U-Net Cerebral Aneurysm Detection -- Detect and Identify Aneurysms Based on Ajusted 3D Attention Unet -- Cerebral Aneurysm Segmentation -- A$\nu$-net: Automatic Detection and Segmentation of Aneurysm -- 3D Attention U-Net with pretraining: A Solution to CADA-Aneurysm Segmentation Challenge -- Exploring Large Context for Cerebral Aneurysm Segmentation -- Cerebral Aneurysm Rupture Risk Estimation -- CADA Challenge: Rupture risk assessment using Computational Fluid Dynamics -- Cerebral Aneurysm Rupture Risk Estimation Using XGBoost and Fully Connected Neural Network -- Intracranial aneurysm rupture risk estimation utilizing vessel-graphs and machine learning -- Intracranial aneurysm rupture prediction with computational fluid dynamics point clouds.

This book constitutes the First Cerebral Aneurysm Detection Challenge, CADA 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in October 2020. The conference was planned to take place in Lima, Peru, and took place virtually due to the COVID-19 pandemic. The 9 regular papers presented in this volume, together with an overview and one introduction paper, were carefully reviewed and selected for inclusion in the book. The papers were organized in topical sections as follows: cerebral aneurysm detection; cerebral aneurysm segmentation; and cerebral aneurysm rupture risk estimation.

ISBN: 9783030728625

Standard No.: 10.1007/978-3-030-72862-5doiSubjects--Topical Terms:

639187
Optical data processing.

LC Class. No.: TA1630-1650

Dewey Class. No.: 006.6

Cerebral Aneurysm Detection and Analysis = First Challenge, CADA 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings /
LDR:03248nam a22004095i 4500 001 1051337
003 DE-He213
005 20210826173314.0
007 cr nn 008mamaa
008 220103s2021 sz | s |||| 0|eng d
020 $a 9783030728625 $9 978-3-030-72862-5
024 7 $a 10.1007/978-3-030-72862-5 $2 doi
035 $a 978-3-030-72862-5
050 4 $a TA1630-1650
072 7 $a UYQV $2 bicssc
072 7 $a COM016000 $2 bisacsh
072 7 $a UYQV $2 thema
082 0 4 $a 006.6 $2 23
245 1 0 $a Cerebral Aneurysm Detection and Analysis $h [electronic resource] : $b First Challenge, CADA 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings / $c edited by Anja Hennemuth, Leonid Goubergrits, Matthias Ivantsits, Jan-Martin Kuhnigk.
250 $a 1st ed. 2021.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a X, 113 p. 47 illus., 41 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Image Processing, Computer Vision, Pattern Recognition, and Graphics ; $v 12643
505 0 $a Overview of the CADA Challenge at MICCAI 2020 -- Cerebral Aneurysm Detection and Analysis Challenge 2020 (CADA) -- Introduction -- CADA: Clinical Background and Motivation -- Cerebral Aneurysm Detection -- Deep Learning-Based 3D U-Net Cerebral Aneurysm Detection -- Detect and Identify Aneurysms Based on Ajusted 3D Attention Unet -- Cerebral Aneurysm Segmentation -- A$\nu$-net: Automatic Detection and Segmentation of Aneurysm -- 3D Attention U-Net with pretraining: A Solution to CADA-Aneurysm Segmentation Challenge -- Exploring Large Context for Cerebral Aneurysm Segmentation -- Cerebral Aneurysm Rupture Risk Estimation -- CADA Challenge: Rupture risk assessment using Computational Fluid Dynamics -- Cerebral Aneurysm Rupture Risk Estimation Using XGBoost and Fully Connected Neural Network -- Intracranial aneurysm rupture risk estimation utilizing vessel-graphs and machine learning -- Intracranial aneurysm rupture prediction with computational fluid dynamics point clouds.
520 $a This book constitutes the First Cerebral Aneurysm Detection Challenge, CADA 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in October 2020. The conference was planned to take place in Lima, Peru, and took place virtually due to the COVID-19 pandemic. The 9 regular papers presented in this volume, together with an overview and one introduction paper, were carefully reviewed and selected for inclusion in the book. The papers were organized in topical sections as follows: cerebral aneurysm detection; cerebral aneurysm segmentation; and cerebral aneurysm rupture risk estimation.
650 0 $a Optical data processing. $3 639187
650 0 $a Application software. $3 528147
650 0 $a Machine learning. $3 561253
650 0 $a Computer science—Mathematics. $3 1253519
650 1 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 671334
650 2 4 $a Computer Applications. $3 669785
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Mathematics of Computing. $3 669457
700 1 $a Hennemuth, Anja. $e editor. $1 https://orcid.org/0000-0002-0737-7375 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1355830
700 1 $a Goubergrits, Leonid. $e editor. $1 https://orcid.org/0000-0002-1961-3179 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1355831
700 1 $a Ivantsits, Matthias. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1355832
700 1 $a Kuhnigk, Jan-Martin. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1355833
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030728618
776 0 8 $i Printed edition: $z 9783030728632
830 0 $a Image Processing, Computer Vision, Pattern Recognition, and Graphics ; $v 9219 $3 1253644
856 4 0 $u https://doi.org/10.1007/978-3-030-72862-5
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
912 $a ZDB-2-LNC
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)