國立虎尾科技大學 |

Simulation and Synthesis in Medical Imaging = 6th International Workshop, SASHIMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Simulation and Synthesis in Medical Imaging/ edited by David Svoboda, Ninon Burgos, Jelmer M. Wolterink, Can Zhao.
Reminder of title:	6th International Workshop, SASHIMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings /
other author:	Svoboda, David.
Description:	X, 154 p. 58 illus., 47 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Optical data processing. -
Online resource:	https://doi.org/10.1007/978-3-030-87592-3
ISBN:	9783030875923

Simulation and Synthesis in Medical Imaging = 6th International Workshop, SASHIMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings /
Simulation and Synthesis in Medical Imaging6th International Workshop, SASHIMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings /[electronic resource] :edited by David Svoboda, Ninon Burgos, Jelmer M. Wolterink, Can Zhao. - 1st ed. 2021. - X, 154 p. 58 illus., 47 illus. in color.online resource. - Image Processing, Computer Vision, Pattern Recognition, and Graphics ;12965. - Image Processing, Computer Vision, Pattern Recognition, and Graphics ;9219.

Method-Oriented Papers -- Detail matters: high-frequency content for realistic synthetic brain MRI generation -- Joint Image and Label Self-Super-Resolution -- Super-resolution by Latent Space Exploration: Training with Poorly-aligned Clinical and Micro CT Image Dataset -- A Glimpse into the Future: Disease Progression Simulation for Breast Cancer in Mammograms -- Synth-by-Reg (SbR): Contrastive learning for synthesis-based registration of paired images -- Learning-based Template Synthesis For Groupwise Image Registration -- The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties -- Transfer Learning in Optical Microscopy -- X-ray synthesis based on triangular mesh models using GPU-accelerated ray tracing for multi-modal breast image registration -- Application-Oriented Papers -- Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections and Generative Adversarial Networks -- SequenceGAN: Generating Fundus Fluorescence Angiography Sequences from Structure Fundus Image -- Cerebral Blood Volume Prediction based on Multi-modality Magnetic Resonance Imaging -- Cine-MRI simulation to evaluate tumor tracking -- GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models.

This book constitutes the refereed proceedings of the 6th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in September 2021.* The 14 full papers presented were carefully reviewed and selected from 18 submissions. The contributions span the following broad categories in alignment with the initial call-for-papers: methods based on generative models or adversarial learning for MRI/CT/ microscopy image synthesis, and several applications of image synthesis and simulation for data augmentation, image enhancement, or segmentation. *The workshop was held virtually.

ISBN: 9783030875923

Standard No.: 10.1007/978-3-030-87592-3doiSubjects--Topical Terms:

639187
Optical data processing.

LC Class. No.: TA1630-1650

Dewey Class. No.: 006.6

Simulation and Synthesis in Medical Imaging = 6th International Workshop, SASHIMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings /
LDR:03661nam a22004335i 4500 001 1055604
003 DE-He213
005 20210919203210.0
007 cr nn 008mamaa
008 220103s2021 sz | s |||| 0|eng d
020 $a 9783030875923 $9 978-3-030-87592-3
024 7 $a 10.1007/978-3-030-87592-3 $2 doi
035 $a 978-3-030-87592-3
050 4 $a TA1630-1650
072 7 $a UYT $2 bicssc
072 7 $a COM012000 $2 bisacsh
072 7 $a UYT $2 thema
072 7 $a UYQV $2 thema
082 0 4 $a 006.6 $2 23
082 0 4 $a 006.37 $2 23
245 1 0 $a Simulation and Synthesis in Medical Imaging $h [electronic resource] : $b 6th International Workshop, SASHIMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings / $c edited by David Svoboda, Ninon Burgos, Jelmer M. Wolterink, Can Zhao.
250 $a 1st ed. 2021.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a X, 154 p. 58 illus., 47 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 12965
505 0 $a Method-Oriented Papers -- Detail matters: high-frequency content for realistic synthetic brain MRI generation -- Joint Image and Label Self-Super-Resolution -- Super-resolution by Latent Space Exploration: Training with Poorly-aligned Clinical and Micro CT Image Dataset -- A Glimpse into the Future: Disease Progression Simulation for Breast Cancer in Mammograms -- Synth-by-Reg (SbR): Contrastive learning for synthesis-based registration of paired images -- Learning-based Template Synthesis For Groupwise Image Registration -- The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties -- Transfer Learning in Optical Microscopy -- X-ray synthesis based on triangular mesh models using GPU-accelerated ray tracing for multi-modal breast image registration -- Application-Oriented Papers -- Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections and Generative Adversarial Networks -- SequenceGAN: Generating Fundus Fluorescence Angiography Sequences from Structure Fundus Image -- Cerebral Blood Volume Prediction based on Multi-modality Magnetic Resonance Imaging -- Cine-MRI simulation to evaluate tumor tracking -- GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models.
520 $a This book constitutes the refereed proceedings of the 6th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in September 2021.* The 14 full papers presented were carefully reviewed and selected from 18 submissions. The contributions span the following broad categories in alignment with the initial call-for-papers: methods based on generative models or adversarial learning for MRI/CT/ microscopy image synthesis, and several applications of image synthesis and simulation for data augmentation, image enhancement, or segmentation. *The workshop was held virtually.
650 0 $a Optical data processing. $3 639187
650 0 $a Artificial intelligence. $3 559380
650 0 $a Pattern recognition. $3 1253525
650 0 $a Bioinformatics. $3 583857
650 1 4 $a Image Processing and Computer Vision. $3 670819
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Pattern Recognition. $3 669796
650 2 4 $a Computational Biology/Bioinformatics. $3 677363
700 1 $a Svoboda, David. $e editor. $1 https://orcid.org/0000-0001-6074-0164 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1297527
700 1 $a Burgos, Ninon. $e editor. $1 https://orcid.org/0000-0002-4668-2006 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1281928
700 1 $a Wolterink, Jelmer M. $e editor. $1 https://orcid.org/0000-0001-5505-475X $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1327914
700 1 $a Zhao, Can. $e editor. $1 https://orcid.org/0000-0001-7286-3452 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1327915
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030875916
776 0 8 $i Printed edition: $z 9783030875930
830 0 $a Image Processing, Computer Vision, Pattern Recognition, and Graphics ; $v 9219 $3 1253644
856 4 0 $u https://doi.org/10.1007/978-3-030-87592-3
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)