國立虎尾科技大學 |

Myocardial Pathology Segmentation Combining Multi-Sequence Cardiac Magnetic Resonance Images = First Challenge, MyoPS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Myocardial Pathology Segmentation Combining Multi-Sequence Cardiac Magnetic Resonance Images/ edited by Xiahai Zhuang, Lei Li.
Reminder of title:	First Challenge, MyoPS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings /
other author:	Li, Lei.
Description:	VIII, 177 p. 91 illus., 77 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computational Biology/Bioinformatics. -
Online resource:	https://doi.org/10.1007/978-3-030-65651-5
ISBN:	9783030656515

Myocardial Pathology Segmentation Combining Multi-Sequence Cardiac Magnetic Resonance Images = First Challenge, MyoPS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings /
Myocardial Pathology Segmentation Combining Multi-Sequence Cardiac Magnetic Resonance ImagesFirst Challenge, MyoPS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings /[electronic resource] :edited by Xiahai Zhuang, Lei Li. - 1st ed. 2020. - VIII, 177 p. 91 illus., 77 illus. in color.online resource. - Image Processing, Computer Vision, Pattern Recognition, and Graphics ;12554. - Image Processing, Computer Vision, Pattern Recognition, and Graphics ;9219.

Stacked BCDU-net with semantic CMR synthesis: application to Myocardial PathologySegmentation challenge -- EfficientSeg: A Simple but Efficient Solution to Myocardial Pathology Segmentation Challenge -- Two-stage Method for Segmentation of the Myocardial Scars and Edema on Multi-sequence Cardiac Magnetic Resonance -- Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images -- Myocardial Edema and Scar Segmentation using a Coarse-to-Fine Framework with Weighted Ensemble -- Exploring ensemble applications for multi-sequence myocardial pathology segmentation -- Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling -- Fully automated deep learning based segmentation of normal, infarcted and edema regions from multiple cardiac MRI sequences -- CMS-UNet: Cardiac Multi-task Segmentation in MRI with a U-shaped Network -- Automatic Myocardial Scar Segmentation from Multi-Sequence Cardiac MRI using Fully Convolutional Densenet with Inception and Squeeze-Excitation Module -- Dual Attention U-net for Multi-Sequence Cardiac MR Images Segmentation -- Accurate Myocardial Pathology Segmentation with Residual U-Net -- Stacked and Parallel U-Nets with Multi-Output for Myocardial Pathology Segmentation -- Dual-path Feature Aggregation Network Combined Multi-layer Fusion for Myocardial Pathology Segmentation with Multi-sequence Cardiac MR -- Cascaded Framework with Complementary CMR Information for Myocardial Pathology Segmentation -- CMRadjustNet: Recognition and standardization of cardiac MRI orientation via multi-tasking learning and deep neural networks.

This book constitutes the First Myocardial Pathology Segmentation Combining Multi-Sequence CMR Challenge, MyoPS 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 crisis. The 12 full and 4 short papers presented in this volume were carefully reviewed and selected form numerous submissions. This challenge aims not only to benchmark various myocardial pathology segmentation algorithms, but also to cover the topic of general cardiac image segmentation, registration and modeling, and raise discussions for further technical development and clinical deployment.

ISBN: 9783030656515

Standard No.: 10.1007/978-3-030-65651-5doiSubjects--Topical Terms:

677363
Computational Biology/Bioinformatics.

LC Class. No.: TA1630-1650

Dewey Class. No.: 006.6

Myocardial Pathology Segmentation Combining Multi-Sequence Cardiac Magnetic Resonance Images = First Challenge, MyoPS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings /
LDR:03975nam a22004095i 4500 001 1030964
003 DE-He213
005 20210216202007.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030656515 $9 978-3-030-65651-5
024 7 $a 10.1007/978-3-030-65651-5 $2 doi
035 $a 978-3-030-65651-5
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 Myocardial Pathology Segmentation Combining Multi-Sequence Cardiac Magnetic Resonance Images $h [electronic resource] : $b First Challenge, MyoPS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings / $c edited by Xiahai Zhuang, Lei Li.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a VIII, 177 p. 91 illus., 77 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 12554
505 0 $a Stacked BCDU-net with semantic CMR synthesis: application to Myocardial PathologySegmentation challenge -- EfficientSeg: A Simple but Efficient Solution to Myocardial Pathology Segmentation Challenge -- Two-stage Method for Segmentation of the Myocardial Scars and Edema on Multi-sequence Cardiac Magnetic Resonance -- Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images -- Myocardial Edema and Scar Segmentation using a Coarse-to-Fine Framework with Weighted Ensemble -- Exploring ensemble applications for multi-sequence myocardial pathology segmentation -- Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling -- Fully automated deep learning based segmentation of normal, infarcted and edema regions from multiple cardiac MRI sequences -- CMS-UNet: Cardiac Multi-task Segmentation in MRI with a U-shaped Network -- Automatic Myocardial Scar Segmentation from Multi-Sequence Cardiac MRI using Fully Convolutional Densenet with Inception and Squeeze-Excitation Module -- Dual Attention U-net for Multi-Sequence Cardiac MR Images Segmentation -- Accurate Myocardial Pathology Segmentation with Residual U-Net -- Stacked and Parallel U-Nets with Multi-Output for Myocardial Pathology Segmentation -- Dual-path Feature Aggregation Network Combined Multi-layer Fusion for Myocardial Pathology Segmentation with Multi-sequence Cardiac MR -- Cascaded Framework with Complementary CMR Information for Myocardial Pathology Segmentation -- CMRadjustNet: Recognition and standardization of cardiac MRI orientation via multi-tasking learning and deep neural networks.
520 $a This book constitutes the First Myocardial Pathology Segmentation Combining Multi-Sequence CMR Challenge, MyoPS 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 crisis. The 12 full and 4 short papers presented in this volume were carefully reviewed and selected form numerous submissions. This challenge aims not only to benchmark various myocardial pathology segmentation algorithms, but also to cover the topic of general cardiac image segmentation, registration and modeling, and raise discussions for further technical development and clinical deployment.
650 2 4 $a Computational Biology/Bioinformatics. $3 677363
650 2 4 $a Computer Appl. in Social and Behavioral Sciences. $3 669920
650 2 4 $a Artificial Intelligence. $3 646849
650 1 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 671334
650 0 $a Bioinformatics. $3 583857
650 0 $a Application software. $3 528147
650 0 $a Artificial intelligence. $3 559380
650 0 $a Optical data processing. $3 639187
700 1 $a Li, Lei. $e editor. $1 https://orcid.org/0000-0003-1281-6472 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1328008
700 1 $a Zhuang, Xiahai. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1210053
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030656508
776 0 8 $i Printed edition: $z 9783030656522
830 0 $a Image Processing, Computer Vision, Pattern Recognition, and Graphics ; $v 9219 $3 1253644
856 4 0 $u https://doi.org/10.1007/978-3-030-65651-5
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)