國立虎尾科技大學 |

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries = Third International Workshop, BrainLes 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Revised Selected Papers /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries/ edited by Alessandro Crimi, Spyridon Bakas, Hugo Kuijf, Bjoern Menze, Mauricio Reyes.
其他題名:	Third International Workshop, BrainLes 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Revised Selected Papers /
其他作者:	Crimi, Alessandro.
面頁冊數:	XIII, 517 p. 233 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Optical data processing. -
電子資源:	https://doi.org/10.1007/978-3-319-75238-9
ISBN:	9783319752389

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries = Third International Workshop, BrainLes 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Revised Selected Papers /
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain InjuriesThird International Workshop, BrainLes 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Revised Selected Papers /[electronic resource] :edited by Alessandro Crimi, Spyridon Bakas, Hugo Kuijf, Bjoern Menze, Mauricio Reyes. - 1st ed. 2018. - XIII, 517 p. 233 illus.online resource. - Image Processing, Computer Vision, Pattern Recognition, and Graphics ;10670. - Image Processing, Computer Vision, Pattern Recognition, and Graphics ;9219.

Invited Talks -- Dice overlap measures for objects of unknown number: Application to lesion segmentation -- Lesion Detection, Segmentation and Prediction in Multiple Sclerosis Clinical Trials -- Brain Lesion Image Analysis -- Automated Segmentation of Multiple Sclerosis Lesions using Multi-Dimensional Gated Recurrent Units -- Joint Intensity Fusion Image Synthesis Applied to Multiple Sclerosis Lesion Segmentation -- MARCEL (inter-Modality Ane Registration with CorELation ratio): An Application for Brain Shift Correction in Ultrasound-Guided Brain Tumor Resection -- Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks -- Overall Survival Time Prediction for High Grade Gliomas based on Sparse Representation Framework -- Traumatic Brain Lesion Quantication based on Mean Diusivity Changes -- Pairwise, Ordinal Outlier Detection of Traumatic Brain Injuries -- Sub-Acute & Chronic Ischemic Stroke Lesion MRI Segmentation -- Brain Tumor Segmentation Using an Adversarial Network -- Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma -- Brain Tumor Image Segmentation -- Deep Learning based Multimodal Brain Tumor Diagnosis -- Multimodal Brain Tumor Segmentation using Ensemble of Forest Method -- Pooling-free fully convolutional networks with dense skip connections for semantic segmentation, with application to brain tumor segmentation -- Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks -- 3D Brain Tumor Segmentation through Integrating Multiple 2D FCNNs -- MRI Brain Tumor Segmentation and Patient Survival Prediction using Random Forests and Fully Convolutional Networks -- Automatic Segmentation and Overall Survival Prediction in Gliomas using Fully Convolutional Neural Network and Texture Analysis -- Multimodal Brain Tumor Segmentation Using 3D Convolutional Networks -- A Conditional Adversarial Network for Semantic Segmentation of Brain Tumor -- Dilated Convolutions for Brain Tumor Segmentation in MRI Scans -- Residual Encoder and Convolutional Decoder Neural Network for Glioma Segmentation -- TPCNN: Two-phase Patch-based Convolutional Neural Network for Automatic Brain Tumor Segmentation and Survival Prediction -- Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge -- Multi-modal PixelNet for Brain Tumor Segmentation -- Brain Tumor Segmentation using Dense Fully Convolutional Neural Network -- Brain Tumor Segmentation in MRI Scans using Deeply-Supervised Neural Networks -- Brain Tumor Segmentation and Parsing on MRIs using Multiresolution Neural Networks -- Brain Tumor Segmentation using Deep Fully Convolutional Neural Networks -- Glioblastoma and Survival Prediction -- MRI Augmentation via Elastic Registration for Brain Lesions Segmentation -- Cascaded V-Net using ROI masks for brain tumor segmentation -- Brain Tumor Segmentation using a 3D FCN with Multi-Scale Loss -- Brain tumor segmentation using a multi-path CNN based method -- 3D Deep Neural Network-Based Brain Tumor Segmentation Using Multimodality Magnetic Resonance Sequences -- Automated Brain Tumor Segmentation on Magnetic Resonance Images (MRIs) and Patient Overall Survival Prediction using Support Vector Machines -- Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation -- Tumor segmentation from multimodal MRI using random forest with superpixel and tensor based feature extraction -- Towards Uncertainty-assisted Brain Tumor Segmentation and Survival Prediction -- Ischemic Stroke Lesion Image Segmentation -- WMH Segmentation Challenge: a Texture-based Classication Approach -- White Matter Hyperintensities Segmentation In a Few Seconds Using Fully Convolutional Network and Transfer Learning.

This book constitutes revised selected papers from the Third International MICCAI Brainlesion Workshop, BrainLes 2017, as well as the International Multimodal Brain Tumor Segmentation, BraTS, and White Matter Hyperintensities, WMH, segmentation challenges, which were held jointly at the Medical Image computing for Computer Assisted Intervention Conference, MICCAI, in Quebec City, Canada, in September 2017. The 40 papers presented in this volume were carefully reviewed and selected from 46 submissions. They were organized in topical sections named: brain lesion image analysis; brain tumor image segmentation; and ischemic stroke lesion image segmentation.

ISBN: 9783319752389

Standard No.: 10.1007/978-3-319-75238-9doiSubjects--Topical Terms:

639187
Optical data processing.

LC Class. No.: TA1630-1650

Dewey Class. No.: 006.6

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries = Third International Workshop, BrainLes 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Revised Selected Papers /
LDR:06213nam a22004335i 4500 001 992293
003 DE-He213
005 20200702113407.0
007 cr nn 008mamaa
008 201225s2018 gw | s |||| 0|eng d
020 $a 9783319752389 $9 978-3-319-75238-9
024 7 $a 10.1007/978-3-319-75238-9 $2 doi
035 $a 978-3-319-75238-9
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 Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries $h [electronic resource] : $b Third International Workshop, BrainLes 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Revised Selected Papers / $c edited by Alessandro Crimi, Spyridon Bakas, Hugo Kuijf, Bjoern Menze, Mauricio Reyes.
250 $a 1st ed. 2018.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a XIII, 517 p. 233 illus. $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 10670
505 0 $a Invited Talks -- Dice overlap measures for objects of unknown number: Application to lesion segmentation -- Lesion Detection, Segmentation and Prediction in Multiple Sclerosis Clinical Trials -- Brain Lesion Image Analysis -- Automated Segmentation of Multiple Sclerosis Lesions using Multi-Dimensional Gated Recurrent Units -- Joint Intensity Fusion Image Synthesis Applied to Multiple Sclerosis Lesion Segmentation -- MARCEL (inter-Modality Ane Registration with CorELation ratio): An Application for Brain Shift Correction in Ultrasound-Guided Brain Tumor Resection -- Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks -- Overall Survival Time Prediction for High Grade Gliomas based on Sparse Representation Framework -- Traumatic Brain Lesion Quantication based on Mean Diusivity Changes -- Pairwise, Ordinal Outlier Detection of Traumatic Brain Injuries -- Sub-Acute & Chronic Ischemic Stroke Lesion MRI Segmentation -- Brain Tumor Segmentation Using an Adversarial Network -- Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma -- Brain Tumor Image Segmentation -- Deep Learning based Multimodal Brain Tumor Diagnosis -- Multimodal Brain Tumor Segmentation using Ensemble of Forest Method -- Pooling-free fully convolutional networks with dense skip connections for semantic segmentation, with application to brain tumor segmentation -- Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks -- 3D Brain Tumor Segmentation through Integrating Multiple 2D FCNNs -- MRI Brain Tumor Segmentation and Patient Survival Prediction using Random Forests and Fully Convolutional Networks -- Automatic Segmentation and Overall Survival Prediction in Gliomas using Fully Convolutional Neural Network and Texture Analysis -- Multimodal Brain Tumor Segmentation Using 3D Convolutional Networks -- A Conditional Adversarial Network for Semantic Segmentation of Brain Tumor -- Dilated Convolutions for Brain Tumor Segmentation in MRI Scans -- Residual Encoder and Convolutional Decoder Neural Network for Glioma Segmentation -- TPCNN: Two-phase Patch-based Convolutional Neural Network for Automatic Brain Tumor Segmentation and Survival Prediction -- Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge -- Multi-modal PixelNet for Brain Tumor Segmentation -- Brain Tumor Segmentation using Dense Fully Convolutional Neural Network -- Brain Tumor Segmentation in MRI Scans using Deeply-Supervised Neural Networks -- Brain Tumor Segmentation and Parsing on MRIs using Multiresolution Neural Networks -- Brain Tumor Segmentation using Deep Fully Convolutional Neural Networks -- Glioblastoma and Survival Prediction -- MRI Augmentation via Elastic Registration for Brain Lesions Segmentation -- Cascaded V-Net using ROI masks for brain tumor segmentation -- Brain Tumor Segmentation using a 3D FCN with Multi-Scale Loss -- Brain tumor segmentation using a multi-path CNN based method -- 3D Deep Neural Network-Based Brain Tumor Segmentation Using Multimodality Magnetic Resonance Sequences -- Automated Brain Tumor Segmentation on Magnetic Resonance Images (MRIs) and Patient Overall Survival Prediction using Support Vector Machines -- Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation -- Tumor segmentation from multimodal MRI using random forest with superpixel and tensor based feature extraction -- Towards Uncertainty-assisted Brain Tumor Segmentation and Survival Prediction -- Ischemic Stroke Lesion Image Segmentation -- WMH Segmentation Challenge: a Texture-based Classication Approach -- White Matter Hyperintensities Segmentation In a Few Seconds Using Fully Convolutional Network and Transfer Learning.
520 $a This book constitutes revised selected papers from the Third International MICCAI Brainlesion Workshop, BrainLes 2017, as well as the International Multimodal Brain Tumor Segmentation, BraTS, and White Matter Hyperintensities, WMH, segmentation challenges, which were held jointly at the Medical Image computing for Computer Assisted Intervention Conference, MICCAI, in Quebec City, Canada, in September 2017. The 40 papers presented in this volume were carefully reviewed and selected from 46 submissions. They were organized in topical sections named: brain lesion image analysis; brain tumor image segmentation; and ischemic stroke lesion image segmentation.
650 0 $a Optical data processing. $3 639187
650 0 $a Artificial intelligence. $3 559380
650 0 $a Mathematical statistics. $3 527941
650 0 $a Health informatics. $3 1064466
650 1 4 $a Image Processing and Computer Vision. $3 670819
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Probability and Statistics in Computer Science. $3 669886
650 2 4 $a Health Informatics. $3 593963
700 1 $a Crimi, Alessandro. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1106621
700 1 $a Bakas, Spyridon. $e editor. $1 https://orcid.org/0000-0001-8734-6482 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1283981
700 1 $a Kuijf, Hugo. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1283982
700 1 $a Menze, Bjoern. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 784599
700 1 $a Reyes, Mauricio. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1267681
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319752372
776 0 8 $i Printed edition: $z 9783319752396
830 0 $a Image Processing, Computer Vision, Pattern Recognition, and Graphics ; $v 9219 $3 1253644
856 4 0 $u https://doi.org/10.1007/978-3-319-75238-9
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)