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The Resource Medical image computing and computer assisted intervention -- MICCAI 2019 : 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part I, Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan (eds.)

Medical image computing and computer assisted intervention -- MICCAI 2019 : 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part I, Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan (eds.)

Creator
Editor
Contributor
Summary
The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019. The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: optical imaging; endoscopy; microscopy. Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression. Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging. Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis. Part V: computer assisted interventions; MIC meets CAI. Part VI: computed tomography; X-ray imaging
Language
eng
Work
Publication
Extent
1 online resource (xxxvii, 819 pages)
Note
  • International conference proceedings
  • Includes author index
Contents
  • Intro; Preface; Organization; Accepted MICCAI 2019 Papers; Awards Presented at MICCAI 2018, Granada, Spain; Contents -- Part I; Optical Imaging; Enhancing OCT Signal by Fusion of GANs: Improving Statistical Power of Glaucoma Clinical Trials; 1 Introduction; 2 Materials and Methods; 2.1 Data; 2.2 Proposed Methodology; 3 Experiments and Results; 3.1 Experimental Setup; 3.2 Results; 4 Discussion and Conclusion; References; A Deep Reinforcement Learning Framework for Frame-by-Frame Plaque Tracking on Intravascular Optical Coherence Tomography Image; 1 Introduction
  • 2 Architecture of the Proposed Framework2.1 Spatio-Temporal Correlation RL Module; 2.2 Aided Plaque Localization and Identification Module; 2.3 Implementation and Application Process; 3 Experiment and Analysis; 4 Conclusion; References; Multi-index Optic Disc Quantification via MultiTask Ensemble Learning; 1 Introduction; 2 Deep Multi-task Forests (DMTFs); 2.1 Architectures of DMTFs; 2.2 Task-Specific Branches with Distribution Regression Forest; 2.3 MultiTask Ensemble Module for Multi-index OD Quantification; 3 Experiments; 4 Conclusion; References
  • Retinal Abnormalities Recognition Using Regional Multitask Learning1 Introduction; 2 Datasets; 3 Methods; 3.1 Macular and Optic-Disc Region Detection; 3.2 Semantic Multitask Learning for Retinal Disease Classification; 4 Experiments; 4.1 Results on Optic-Disc and Macula Joint Detection; 4.2 Quantitative Evaluation on Multitask Learning; 4.3 Visualization; 5 Conclusion; References; Unifying Structure Analysis and Surrogate-Driven Function Regression for Glaucoma OCT Image Screening; 1 Introduction; 2 Method; 2.1 Surrogate-Driven Labelling with Semi-supervised Learning
  • 2.2 Multi-task Learning for Structure and Function Analysis3 Experiments and Results; 4 Conclusion; References; Evaluation of Retinal Image Quality Assessment Networks in Different Color-Spaces; 1 Introduction; 2 Eye-Quality Dataset; 3 Multiple Color-Space Fusion Network; 4 Experiments; 5 Conclusion; References; 3D Surface-Based Geometric and Topological Quantification of Retinal Microvasculature in OCT-Angiography via Reeb Analysis; 1 Introduction; 2 Methodology; 2.1 3D Surface Representation of OCT-A Microvasculature
  • 2.2 Intrinsic Reeb Graph Construction Using Geodesic Distance Transform on Vessel Mesh Surfaces2.3 Geometric and Topological Feature Extraction via Reeb Analysis; 3 Experimental Results; 4 Conclusion; References; Limited-Angle Diffuse Optical Tomography Image Reconstruction Using Deep Learning; 1 Introduction; 2 Methodology; 2.1 Background; 2.2 Deep Learning Reconstruction; 3 Experiments and Results; 3.1 Dataset; 3.2 Implementation; 3.3 Qualitative Results; 3.4 Quantitative Results; 4 Conclusion; References; Data-Driven Enhancement of Blurry Retinal Images via Generative Adversarial Networks
Isbn
9783030322397
Instance
Label
Medical image computing and computer assisted intervention -- MICCAI 2019 : 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part I
Title
Medical image computing and computer assisted intervention -- MICCAI 2019
Title remainder
22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings
Title number
Part I
Statement of responsibility
Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan (eds.)
Creator
Contributor
Editor
Subject
Genre
Language
eng
Summary
The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019. The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: optical imaging; endoscopy; microscopy. Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression. Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging. Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis. Part V: computer assisted interventions; MIC meets CAI. Part VI: computed tomography; X-ray imaging
Member of
Cataloging source
GW5XE
Dewey number
616.07/57
Illustrations
illustrations
Index
index present
LC call number
RC78.7.D53
Literary form
non fiction
http://bibfra.me/vocab/lite/meetingDate
2019
http://bibfra.me/vocab/lite/meetingName
International Conference on Medical Image Computing and Computer-Assisted Intervention
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorDate
1948 January 5-
http://library.link/vocab/relatedWorkOrContributorName
  • Shen, Dinggang
  • Liu, Tianming
  • Peters, Terry M.
  • Staib, Lawrence
  • Essert, Caroline
  • Zhou, Xiangyun Sean
  • Yap, Pew-Thian
  • Khan, Ali
Series statement
  • Lecture notes in computer science
  • LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics
Series volume
11764
http://library.link/vocab/subjectName
  • Diagnostic imaging
  • Computer-assisted surgery
Label
Medical image computing and computer assisted intervention -- MICCAI 2019 : 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part I, Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan (eds.)
Instantiates
Publication
Note
  • International conference proceedings
  • Includes author index
Antecedent source
unknown
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Intro; Preface; Organization; Accepted MICCAI 2019 Papers; Awards Presented at MICCAI 2018, Granada, Spain; Contents -- Part I; Optical Imaging; Enhancing OCT Signal by Fusion of GANs: Improving Statistical Power of Glaucoma Clinical Trials; 1 Introduction; 2 Materials and Methods; 2.1 Data; 2.2 Proposed Methodology; 3 Experiments and Results; 3.1 Experimental Setup; 3.2 Results; 4 Discussion and Conclusion; References; A Deep Reinforcement Learning Framework for Frame-by-Frame Plaque Tracking on Intravascular Optical Coherence Tomography Image; 1 Introduction
  • 2 Architecture of the Proposed Framework2.1 Spatio-Temporal Correlation RL Module; 2.2 Aided Plaque Localization and Identification Module; 2.3 Implementation and Application Process; 3 Experiment and Analysis; 4 Conclusion; References; Multi-index Optic Disc Quantification via MultiTask Ensemble Learning; 1 Introduction; 2 Deep Multi-task Forests (DMTFs); 2.1 Architectures of DMTFs; 2.2 Task-Specific Branches with Distribution Regression Forest; 2.3 MultiTask Ensemble Module for Multi-index OD Quantification; 3 Experiments; 4 Conclusion; References
  • Retinal Abnormalities Recognition Using Regional Multitask Learning1 Introduction; 2 Datasets; 3 Methods; 3.1 Macular and Optic-Disc Region Detection; 3.2 Semantic Multitask Learning for Retinal Disease Classification; 4 Experiments; 4.1 Results on Optic-Disc and Macula Joint Detection; 4.2 Quantitative Evaluation on Multitask Learning; 4.3 Visualization; 5 Conclusion; References; Unifying Structure Analysis and Surrogate-Driven Function Regression for Glaucoma OCT Image Screening; 1 Introduction; 2 Method; 2.1 Surrogate-Driven Labelling with Semi-supervised Learning
  • 2.2 Multi-task Learning for Structure and Function Analysis3 Experiments and Results; 4 Conclusion; References; Evaluation of Retinal Image Quality Assessment Networks in Different Color-Spaces; 1 Introduction; 2 Eye-Quality Dataset; 3 Multiple Color-Space Fusion Network; 4 Experiments; 5 Conclusion; References; 3D Surface-Based Geometric and Topological Quantification of Retinal Microvasculature in OCT-Angiography via Reeb Analysis; 1 Introduction; 2 Methodology; 2.1 3D Surface Representation of OCT-A Microvasculature
  • 2.2 Intrinsic Reeb Graph Construction Using Geodesic Distance Transform on Vessel Mesh Surfaces2.3 Geometric and Topological Feature Extraction via Reeb Analysis; 3 Experimental Results; 4 Conclusion; References; Limited-Angle Diffuse Optical Tomography Image Reconstruction Using Deep Learning; 1 Introduction; 2 Methodology; 2.1 Background; 2.2 Deep Learning Reconstruction; 3 Experiments and Results; 3.1 Dataset; 3.2 Implementation; 3.3 Qualitative Results; 3.4 Quantitative Results; 4 Conclusion; References; Data-Driven Enhancement of Blurry Retinal Images via Generative Adversarial Networks
Dimensions
unknown
Extent
1 online resource (xxxvii, 819 pages)
File format
unknown
Form of item
online
Isbn
9783030322397
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
  • 10.1007/978-3-030-32239-7
  • 10.1007/978-3-030-32
Other physical details
illustrations (some color)
http://library.link/vocab/ext/overdrive/overdriveId
com.springer.onix.9783030322397
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)1123174681
Label
Medical image computing and computer assisted intervention -- MICCAI 2019 : 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part I, Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan (eds.)
Publication
Note
  • International conference proceedings
  • Includes author index
Antecedent source
unknown
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Intro; Preface; Organization; Accepted MICCAI 2019 Papers; Awards Presented at MICCAI 2018, Granada, Spain; Contents -- Part I; Optical Imaging; Enhancing OCT Signal by Fusion of GANs: Improving Statistical Power of Glaucoma Clinical Trials; 1 Introduction; 2 Materials and Methods; 2.1 Data; 2.2 Proposed Methodology; 3 Experiments and Results; 3.1 Experimental Setup; 3.2 Results; 4 Discussion and Conclusion; References; A Deep Reinforcement Learning Framework for Frame-by-Frame Plaque Tracking on Intravascular Optical Coherence Tomography Image; 1 Introduction
  • 2 Architecture of the Proposed Framework2.1 Spatio-Temporal Correlation RL Module; 2.2 Aided Plaque Localization and Identification Module; 2.3 Implementation and Application Process; 3 Experiment and Analysis; 4 Conclusion; References; Multi-index Optic Disc Quantification via MultiTask Ensemble Learning; 1 Introduction; 2 Deep Multi-task Forests (DMTFs); 2.1 Architectures of DMTFs; 2.2 Task-Specific Branches with Distribution Regression Forest; 2.3 MultiTask Ensemble Module for Multi-index OD Quantification; 3 Experiments; 4 Conclusion; References
  • Retinal Abnormalities Recognition Using Regional Multitask Learning1 Introduction; 2 Datasets; 3 Methods; 3.1 Macular and Optic-Disc Region Detection; 3.2 Semantic Multitask Learning for Retinal Disease Classification; 4 Experiments; 4.1 Results on Optic-Disc and Macula Joint Detection; 4.2 Quantitative Evaluation on Multitask Learning; 4.3 Visualization; 5 Conclusion; References; Unifying Structure Analysis and Surrogate-Driven Function Regression for Glaucoma OCT Image Screening; 1 Introduction; 2 Method; 2.1 Surrogate-Driven Labelling with Semi-supervised Learning
  • 2.2 Multi-task Learning for Structure and Function Analysis3 Experiments and Results; 4 Conclusion; References; Evaluation of Retinal Image Quality Assessment Networks in Different Color-Spaces; 1 Introduction; 2 Eye-Quality Dataset; 3 Multiple Color-Space Fusion Network; 4 Experiments; 5 Conclusion; References; 3D Surface-Based Geometric and Topological Quantification of Retinal Microvasculature in OCT-Angiography via Reeb Analysis; 1 Introduction; 2 Methodology; 2.1 3D Surface Representation of OCT-A Microvasculature
  • 2.2 Intrinsic Reeb Graph Construction Using Geodesic Distance Transform on Vessel Mesh Surfaces2.3 Geometric and Topological Feature Extraction via Reeb Analysis; 3 Experimental Results; 4 Conclusion; References; Limited-Angle Diffuse Optical Tomography Image Reconstruction Using Deep Learning; 1 Introduction; 2 Methodology; 2.1 Background; 2.2 Deep Learning Reconstruction; 3 Experiments and Results; 3.1 Dataset; 3.2 Implementation; 3.3 Qualitative Results; 3.4 Quantitative Results; 4 Conclusion; References; Data-Driven Enhancement of Blurry Retinal Images via Generative Adversarial Networks
Dimensions
unknown
Extent
1 online resource (xxxvii, 819 pages)
File format
unknown
Form of item
online
Isbn
9783030322397
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
  • 10.1007/978-3-030-32239-7
  • 10.1007/978-3-030-32
Other physical details
illustrations (some color)
http://library.link/vocab/ext/overdrive/overdriveId
com.springer.onix.9783030322397
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)1123174681

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