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• Part I: Image Quality and Artefacts
• Image Reconstruction Methods
• Machine Learning in Medical Imaging
• Statistical Analysis for Medical Imaging
• Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications
• Histology Applications
• Microscopy Applications
• Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications
• Lung Imaging Applications
• Breast Imaging Applications
• Other Abdominal Applications. Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging
• Diffusion Weighted Imaging
• Functional MRI
• Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging
• Brain Segmentation Methods. Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery
• Surgical Planning, Simulation and Work Flow Analysis
• Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications
• Multi-Organ Segmentation
• Abdominal Segmentation Methods
• Cardiac Segmentation Methods
• Chest, Lung and Spine Segmentation
• Other Segmentation Applications.
• (source: Nielsen Book Data)

• Special LNCS price list
• Frontmatter
• No extra bibliographic information, no special copyright line, nor logos to be included.
• All standards of the selected production classification to be applied.
• LNCS format
• Precursor Volume: 10433-10435
• Order Series: ---
• Preface - starts on a right page
• Organization pages - start on a right page
• TOC - starts on a right page
• Please insert the line breaks in the title on p. III as follows:
• Medical Image Computing \\
• and Computer-Assisted Intervention - \\
• MICCAI 2018\\
• Please insert the line breaks in the subtitle on p. III as follows:
• 21st International Conference\\
• Granada, Spain, September 16-20, 2018\\
• Proceedings, Part II
• Copyediting
• All standards of the selected CE Level to be applied consistently within the individual chapters (i.e. no extra instructions regarding math mark-up, styling references, citations, etc.).
• LNCS Sublibrary: 6/7412
• You get the edited preface and the organization pages within one week directly from Isabella.
• Proofs
• Send proofs to the corresponding originator.
• Layout
• For projects in production category D: apply a global layout with standard global (series) options. As regards the numbering of headings, please follow the manuscript. Return full-text XML.
• Source line chapter opening page:
• Fulltext-XML
• (c) Springer Nature Switzerland AG 2018\\ A.F. Frangi et al. (Eds.): MICCAI 2018, LNCS 11070/11071/11072/11073, pp. X-XY, 2018\\
• DOI: 10.1007/978-3-030-00000-0_z \\
• Ads
• No internal no external ads to be included anywhere in the book.
• Cover design specs
• No individual illustration, author details or photo to go on the cover. Apply corporate cover design from http://bookcovers.springer.com/-- for a series volume select the appropriate "Series" template, for a non-series book choose one of the subject specific "Standalone Title" templates.
• LNCS cover grey/red
• Please insert the conference logo on cover page 1.
• Please insert the line breaks in the title on cover page 1as follows:
• Medical Image Computing \\ and Computer-Assisted Intervention - \\
• MICCAI 2018\\
• Please insert the line breaks in the subtitle on cover page 1as follows:
• 21st International Conference\\
• Granada, Spain, September 16-20, 2018\\
• Proceedings, Part I/II/III/IV
• Manuscript Material
• Manuscript files and reference pdf are complete.
• Send proofs to the corresponding originator.
• Corresponding editor: Julia A. Schnabel (email: Julia.schnabel@kcl.ac.uk)
• Complimentary copies
• Handling of complimentary copies is organized by publishing.
• Index(es)
• The manuscript material holds index terms with page numbers-- default index type "combined name/subject index" to be applied.
• Please prepare a common Author Index for the 4 volumes.
• Author index - starts on a right page.
• Miscellaneous
• Other: no other specific requirements with regards to content preparation, project management, manufacturing (special binding, lamination, etc.).
• Precursor Volume: 10433-10435
• Order Series: 7310
• Springer.com
• Use standard material for publication on product site at www.springer.com-- table of contents, preface and second chapter/contribution.
• Sublibrary: 6/7412
• Main fields: I22021
• Keywords ???
• Infotext
• The four-volume set LNCS 11070, 11071, 11072, and 11073 constitutes the refereed proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, held in Granada, Spain, in September 2018.
• The 373 revised full papers presented were carefully reviewed and selected from 1068 submissions in a double-blind review process. The papers have been organized in the following topical sections: Part I: Image Quality and Artefacts-- Image Reconstruction Methods-- Machine Learning in Medical Imaging-- Statistical Analysis for Medical Imaging-- Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications-- Histology Applications-- Microscopy Applications-- Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications-- Lung Imaging Applications-- Breast Imaging Applications-- Other Abdominal Applications.
• Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging-- Diffusion Weighted Imaging-- Functional MRI-- Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging-- Brain Segmentation Methods.
• Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery-- Surgical Planning, Simulation and Work Flow Analysis-- Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications-- Multi-Organ Segmentation-- Abdominal Segmentation Methods-- Cardiac Segmentation Methods-- Chest, Lung and Spine Segmentation-- Other Segmentation Applications.
• SEO
• The MICCAI 2018 proceedings volumes present papers focusing on Reconstruction and Image Quality, Machine Learning and Statistical Analysis, Registration and Image Guidance, Optical and Histology Applications, Chest and Abdominal Applications, fMRI and Diffusion Imaging.
• Short TOC
• Part I: Image Quality and Artefacts-- Image Reconstruction Methods-- Machine Learning in Medical Imaging-- Statistical Analysis for Medical Imaging-- Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications-- Histology Applications-- Microscopy Applications-- Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications-- Lung Imaging Applications-- Breast Imaging Applications-- Other Abdominal Applications.
• Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging-- Diffusion Weighted Imaging-- Functional MRI-- Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging-- Brain Segmentation Methods.
• Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery-- Surgical Planning, Simulation and Work Flow Analysis-- Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications-- Multi-Organ Segmentation-- Abdominal Segmentation Methods-- Cardiac Segmentation Methods-- Chest, Lung and Spine Segmentation-- Other Segmentation Applications.
• (source: Nielsen Book Data)

The four-volume set LNCS 11070, 11071, 11072, and 11073 constitutes the refereed proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, held in Granada, Spain, in September 2018. The 373 revised full papers presented were carefully reviewed and selected from 1068 submissions in a double-blind review process. The papers have been organized in the following topical sections: Part I: Image Quality and Artefacts; Image Reconstruction Methods; Machine Learning in Medical Imaging; Statistical Analysis for Medical Imaging; Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications; Histology Applications; Microscopy Applications; Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications; Lung Imaging Applications; Breast Imaging Applications; Other Abdominal Applications. Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging; Diffusion Weighted Imaging; Functional MRI; Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging; Brain Segmentation Methods. Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery; Surgical Planning, Simulation and Work Flow Analysis; Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications; Multi-Organ Segmentation; Abdominal Segmentation Methods; Cardiac Segmentation Methods; Chest, Lung and Spine Segmentation; Other Segmentation Applications.
(source: Nielsen Book Data)

• Part I: Image Quality and Artefacts
• Image Reconstruction Methods
• Machine Learning in Medical Imaging
• Statistical Analysis for Medical Imaging
• Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications
• Histology Applications
• Microscopy Applications
• Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications
• Lung Imaging Applications
• Breast Imaging Applications
• Other Abdominal Applications. Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging
• Diffusion Weighted Imaging
• Functional MRI
• Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging
• Brain Segmentation Methods. Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery
• Surgical Planning, Simulation and Work Flow Analysis
• Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications
• Multi-Organ Segmentation
• Abdominal Segmentation Methods
• Cardiac Segmentation Methods
• Chest, Lung and Spine Segmentation
• Other Segmentation Applications. .
• (source: Nielsen Book Data)

The four-volume set LNCS 11070, 11071, 11072, and 11073 constitutes the refereed proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, held in Granada, Spain, in September 2018. The 373 revised full papers presented were carefully reviewed and selected from 1068 submissions in a double-blind review process. The papers have been organized in the following topical sections: Part I: Image Quality and Artefacts; Image Reconstruction Methods; Machine Learning in Medical Imaging; Statistical Analysis for Medical Imaging; Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications; Histology Applications; Microscopy Applications; Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications; Lung Imaging Applications; Breast Imaging Applications; Other Abdominal Applications. Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging; Diffusion Weighted Imaging; Functional MRI; Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging; Brain Segmentation Methods. Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery; Surgical Planning, Simulation and Work Flow Analysis; Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications; Multi-Organ Segmentation; Abdominal Segmentation Methods; Cardiac Segmentation Methods; Chest, Lung and Spine Segmentation; Other Segmentation Applications. .
(source: Nielsen Book Data)

• Part I: Image Quality and Artefacts-- Image Reconstruction Methods-- Machine Learning in Medical Imaging-- Statistical Analysis for Medical Imaging-- Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications-- Histology Applications-- Microscopy Applications-- Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications-- Lung Imaging Applications-- Breast Imaging Applications-- Other Abdominal Applications.
• Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging-- Diffusion Weighted Imaging-- Functional MRI-- Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging-- Brain Segmentation Methods.
• Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery-- Surgical Planning, Simulation and Work Flow Analysis-- Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications-- Multi-Organ Segmentation-- Abdominal Segmentation Methods-- Cardiac Segmentation Methods-- Chest, Lung and Spine Segmentation-- Other Segmentation Applications. .
• (source: Nielsen Book Data)

The four-volume set LNCS 11070, 11071, 11072, and 11073 constitutes the refereed proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, held in Granada, Spain, in September 2018. The 373 revised full papers presented were carefully reviewed and selected from 1068 submissions in a double-blind review process. The papers have been organized in the following topical sections: Part I: Image Quality and Artefacts; Image Reconstruction Methods; Machine Learning in Medical Imaging; Statistical Analysis for Medical Imaging; Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications; Histology Applications; Microscopy Applications; Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications; Lung Imaging Applications; Breast Imaging Applications; Other Abdominal Applications. Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging; Diffusion Weighted Imaging; Functional MRI; Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging; Brain Segmentation Methods. Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery; Surgical Planning, Simulation and Work Flow Analysis; Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications; Multi-Organ Segmentation; Abdominal Segmentation Methods; Cardiac Segmentation Methods; Chest, Lung and Spine Segmentation; Other Segmentation Applications. .
(source: Nielsen Book Data)

• Special LNCS price list
• Frontmatter
• No extra bibliographic information, no special copyright line, nor logos to be included.
• All standards of the selected production classification to be applied.
• LNCS format
• Precursor Volume: 10433-10435
• Order Series: ---
• Preface - starts on a right page
• Organization pages - start on a right page
• TOC - starts on a right page
• Please insert the line breaks in the title on p. III as follows:
• Medical Image Computing \\
• and Computer-Assisted Intervention - \\
• MICCAI 2018\\
• Please insert the line breaks in the subtitle on p. III as follows:
• 21st International Conference\\
• Granada, Spain, September 16-20, 2018\\
• Proceedings, Part II
• Copyediting
• All standards of the selected CE Level to be applied consistently within the individual chapters (i.e. no extra instructions regarding math mark-up, styling references, citations, etc.).
• LNCS Sublibrary: 6/7412
• You get the edited preface and the organization pages within one week directly from Isabella.
• Proofs
• Send proofs to the corresponding originator.
• Layout
• For projects in production category D: apply a global layout with standard global (series) options. As regards the numbering of headings, please follow the manuscript. Return full-text XML.
• Source line chapter opening page:
• Fulltext-XML
• (c) Springer Nature Switzerland AG 2018\\ A.F. Frangi et al. (Eds.): MICCAI 2018, LNCS 11070/11071/11072/11073, pp. X-XY, 2018\\
• DOI: 10.1007/978-3-030-00000-0_z \\
• Ads
• No internal no external ads to be included anywhere in the book.
• Cover design specs
• No individual illustration, author details or photo to go on the cover. Apply corporate cover design from http://bookcovers.springer.com/-- for a series volume select the appropriate "Series" template, for a non-series book choose one of the subject specific "Standalone Title" templates.
• LNCS cover grey/red
• Please insert the conference logo on cover page 1.
• Please insert the line breaks in the title on cover page 1as follows:
• Medical Image Computing \\ and Computer-Assisted Intervention - \\
• MICCAI 2018\\
• Please insert the line breaks in the subtitle on cover page 1as follows:
• 21st International Conference\\
• Granada, Spain, September 16-20, 2018\\
• Proceedings, Part I/II/III/IV
• Manuscript Material
• Manuscript files and reference pdf are complete.
• Send proofs to the corresponding originator.
• Corresponding editor: Julia A. Schnabel (email: Julia.schnabel@kcl.ac.uk)
• Complimentary copies
• Handling of complimentary copies is organized by publishing.
• Index(es)
• The manuscript material holds index terms with page numbers-- default index type "combined name/subject index" to be applied.
• Please prepare a common Author Index for the 4 volumes.
• Author index - starts on a right page.
• Miscellaneous
• Other: no other specific requirements with regards to content preparation, project management, manufacturing (special binding, lamination, etc.).
• Precursor Volume: 10433-10435
• Order Series: 7310
• Springer.com
• Use standard material for publication on product site at www.springer.com-- table of contents, preface and second chapter/contribution.
• Sublibrary: 6/7412
• Main fields: I22021
• Keywords ???
• Infotext
• The four-volume set LNCS 11070, 11071, 11072, and 11073 constitutes the refereed proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, held in Granada, Spain, in September 2018.
• The 373 revised full papers presented were carefully reviewed and selected from 1068 submissions in a double-blind review process. The papers have been organized in the following topical sections: Part I: Image Quality and Artefacts-- Image Reconstruction Methods-- Machine Learning in Medical Imaging-- Statistical Analysis for Medical Imaging-- Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications-- Histology Applications-- Microscopy Applications-- Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications-- Lung Imaging Applications-- Breast Imaging Applications-- Other Abdominal Applications.
• Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging-- Diffusion Weighted Imaging-- Functional MRI-- Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging-- Brain Segmentation Methods.
• Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery-- Surgical Planning, Simulation and Work Flow Analysis-- Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications-- Multi-Organ Segmentation-- Abdominal Segmentation Methods-- Cardiac Segmentation Methods-- Chest, Lung and Spine Segmentation-- Other Segmentation Applications.
• SEO
• The MICCAI 2018 proceedings volumes present papers focusing on Reconstruction and Image Quality, Machine Learning and Statistical Analysis, Registration and Image Guidance, Optical and Histology Applications, Chest and Abdominal Applications, fMRI and Diffusion Imaging.
• Short TOC
• Part I: Image Quality and Artefacts-- Image Reconstruction Methods-- Machine Learning in Medical Imaging-- Statistical Analysis for Medical Imaging-- Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications-- Histology Applications-- Microscopy Applications-- Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications-- Lung Imaging Applications-- Breast Imaging Applications-- Other Abdominal Applications.
• Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging-- Diffusion Weighted Imaging-- Functional MRI-- Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging-- Brain Segmentation Methods.
• Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery-- Surgical Planning, Simulation and Work Flow Analysis-- Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications-- Multi-Organ Segmentation-- Abdominal Segmentation Methods-- Cardiac Segmentation Methods-- Chest, Lung and Spine Segmentation-- Other Segmentation Applications.
• .
• (source: Nielsen Book Data)

The four-volume set LNCS 11070, 11071, 11072, and 11073 constitutes the refereed proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, held in Granada, Spain, in September 2018. The 373 revised full papers presented were carefully reviewed and selected from 1068 submissions in a double-blind review process. The papers have been organized in the following topical sections: Part I: Image Quality and Artefacts; Image Reconstruction Methods; Machine Learning in Medical Imaging; Statistical Analysis for Medical Imaging; Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications; Histology Applications; Microscopy Applications; Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications; Lung Imaging Applications; Breast Imaging Applications; Other Abdominal Applications. Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging; Diffusion Weighted Imaging; Functional MRI; Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging; Brain Segmentation Methods. Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery; Surgical Planning, Simulation and Work Flow Analysis; Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications; Multi-Organ Segmentation; Abdominal Segmentation Methods; Cardiac Segmentation Methods; Chest, Lung and Spine Segmentation; Other Segmentation Applications. .
(source: Nielsen Book Data)

• Part I: Image Quality and Artefacts-- Image Reconstruction Methods-- Machine Learning in Medical Imaging-- Statistical Analysis for Medical Imaging-- Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications-- Histology Applications-- Microscopy Applications-- Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications-- Lung Imaging Applications-- Breast Imaging Applications-- Other Abdominal Applications.
• Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging-- Diffusion Weighted Imaging-- Functional MRI-- Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging-- Brain Segmentation Methods.
• Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery-- Surgical Planning, Simulation and Work Flow Analysis-- Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications-- Multi-Organ Segmentation-- Abdominal Segmentation Methods-- Cardiac Segmentation Methods-- Chest, Lung and Spine Segmentation-- Other Segmentation Applications. .
• (source: Nielsen Book Data)

The four-volume set LNCS 11070, 11071, 11072, and 11073 constitutes the refereed proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, held in Granada, Spain, in September 2018. The 373 revised full papers presented were carefully reviewed and selected from 1068 submissions in a double-blind review process. The papers have been organized in the following topical sections: Part I: Image Quality and Artefacts; Image Reconstruction Methods; Machine Learning in Medical Imaging; Statistical Analysis for Medical Imaging; Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications; Histology Applications; Microscopy Applications; Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications; Lung Imaging Applications; Breast Imaging Applications; Other Abdominal Applications. Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging; Diffusion Weighted Imaging; Functional MRI; Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging; Brain Segmentation Methods. Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery; Surgical Planning, Simulation and Work Flow Analysis; Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications; Multi-Organ Segmentation; Abdominal Segmentation Methods; Cardiac Segmentation Methods; Chest, Lung and Spine Segmentation; Other Segmentation Applications. .
(source: Nielsen Book Data)

• Deep learning for magnetic resonance imaging.- Deep learning for computed tomography.- Deep learning for general image reconstruction.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the First International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 17 full papers presented were carefully reviewed and selected from 21 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography, and deep learning for general image reconstruction.
(source: Nielsen Book Data)

• Intro
• Preface
• Organization
• Contents
• Developing Novel Weighted Correlation Kernels for Convolutional Neural Networks to Extract Hierarchical Functional Connectivities from fMRI for Disease Diagnosis
• 1 Introduction
• 2 Method
• 2.1 Subjects and Image Preprocessing
• 2.2 Proposed Weighted Correlation Kernel
• 2.3 Architecture of the Proposed Wc-Kernel Based CNN
• 3 Experiments
• 4 Conclusion
• References
• Robust Contextual Bandit via the Capped-2 Norm for Mobile Health Intervention
• 1 Introduction
• 2 Preliminaries
• 3 Robust Contextual Bandit with Capped-2 Norm

• 3.1 Algorithm for the Critic Updating
• 3.2 Algorithm for the Actor Updating
• 4 Experiments
• 4.1 Datasets
• 4.2 Experiments Settings
• 4.3 Results and Discussion
• 5 Conclusions and Future Directions
• References
• Dynamic Multi-scale CNN Forest Learning for Automatic Cervical Cancer Segmentation
• Abstract
• 1 Introduction
• 2 Proposed Cluster-Based Dynamic Multi-scale Dynamic Forest
• 2.1 Root Node CNN Architecture
• 2.2 Cascaded CNNs
• 2.3 Proposed CNN-Based Dynamic Multi-scale Tree (DMT)
• 2.4 Proposed CK+1DMF Learning Framework
• 3 Results and Discussion
• 4 Conclusion

• 3.2 Training Parameters
• 3.3 Evaluation
• 4 Results
• 4.1 FROC Analysis
• 4.2 Reconstructed Images
• 5 Conclusion and Discussion
• References
• CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement
• 1 Introduction
• 2 Methods
• 2.1 CT Image Enhancement
• 2.2 Lesion Segmentation
• 3 Experimental Results and Analyses
• 4 Conclusions
• References
• Deep Learning Based Inter-modality Image Registration Supervised by Intra-modality Similarity
• Abstract
• 1 Introduction
• 2 Method

• 2.1 Loss Function Based on Intra-modality Similarity
• 2.2 Inter-modality Registration Network
• 2.3 Spatial Transformation Layer
• 3 Experimental Results
• 3.1 Registration Results
• 4 Conclusion
• References
• Regional Abnormality Representation Learning in Structural MRI for AD/MCI Diagnosis
• 1 Introduction
• 2 Materials and Preprocessing
• 3 Proposed Method
• 3.1 Regional Abnormality Representation
• 3.2 Brain-Wise Feature Extraction and Classifier Learning
• 4 Experimental Settings and Results
• 4.1 Experimental Settings
• 4.2 Results and Discussion
• 5 Conclusion

This book constitutes the proceedings of the 9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018, held in conjunction with MICCAI 2018 in Granada, Spain, in September 2018.The 45 papers presented in this volume were carefully reviewed and selected from 82 submissions. They focus on major trends and challenges in the area of machine learning in medical imaging and aim to identify new cutting-edge techniques and their use in medical imaging.
(source: Nielsen Book Data)

• Medical Image Synthesis for Data Augmentation and Anonymization Using Generative Adversarial Networks.- Data Augmentation Using synthetic Lesions Improves Machine Learning Detection of Microbleeds from MRI.- Deep Harmonization of Inconsistent MR Data for Consistent Volume Segmentation.- Cross-modality Image Synthesis from Unpaired Data Using CycleGAN: Effects of Gradient Consistency Loss and Training Data Size.- A Machine Learning Approach to Diffusion MRI Partial Volume Estimation.- Unsupervised Learning for Cross-domain Medical Image Synthesis Using Deformation Invariant Cycle Consistency Networks.- Deep Boosted Regression for MR TO CT Synthesis.- Model-Based Generation of Synthetic 3D Time-Lapse Sequences of Multiple Mutually Interacting Motile Cells with Filopodia.- MRI to FDG-PET: Cross-Modal Synthesis Using 3D U-Net for Multi-Modal Alzheimer's Classification.- Tubular Network Formation Process Using 3D Cellular Potts Model.- Deep Learning Based Coronary Artery Motion Artifact Compensation Using Style-Transfer Synthesis in CT Images.- Lung Nodule Synthesis Using CNN-based Latent Data Representation.- RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours.- Generating Magnetic Resonance Spectroscopy Imaging Data of Brain Tumours from Linear, Non-Linear and Deep Learning Models. .
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the Third International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 14 full papers presented were carefully reviewed and selected from numerous submissions. This workshop continues to provide a state-of-the-art and integrative perspective on simulation and synthesis in medical imaging for the purpose of invigorating research and stimulating new ideas on how to build theoretical links, practical synergies, and best practices between these two research directions.
(source: Nielsen Book Data)

• Spinal Cord Gray Matter-White Matter Segmentation on Magnetic Resonance AMIRA Images with MD-GRU.- Predicting Scoliosis in DXA Scans Using Intermediate Representations.- Fast Registration of CT with Intra-operative Ultrasound Images for Spine Surgery.- Automated Grading of Modic Changes Using CNNs - Improving the Performance with Mix-up.- Error Estimation for Appearance Model Segmentation of Musculoskeletal Structures Using Multiple, Independent Sub-models.- Automated Segmentation of Intervertebral Disc using Fully Dilated Separable Deep Neural Networks.- Intensity Standardization of Skeleton in Follow-up Whole-Body MRI.- Towards a Deformable Multi-Surface Approach to Ligamentous Spine Models for Predictive Simulation-Based Scoliosis Surgery Planning.- Intervertebral Disc Segmentation Using Mathematical Morphology-A CNN-Free Approach.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 5th International Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, CSI 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 8 full papers presented together with 8 short papers and 1 keynote were carefully reviewed and selected for inclusion in this volume. Papers on novel methodology and clinical research, and also papers which demonstrate the performance of methods on the provided challenges, the aim is to cover both theoretical and very practical aspects of computerized spinal imaging. .
(source: Nielsen Book Data)

• Computer Aided Identification of Motion Disturbances Related to Parkinson's Disease
• Prediction of Severity and Treatment Outcome for ASD from fMRI
• Enhancement of Perivascular Spaces Using a Very Deep 3D Dense Network
• Generation of Amyloid PET Images via Conditional Adversarial Training for Predicting Progression to Alzheimer's Disease
• Prediction of Hearing Loss Based on Auditory Perception: A Preliminary Study
• Predictive Patient Care: Survival Model to Prevent Medication Non-adherence
• Joint Robust Imputation and Classification for Early Dementia Detection Using Incomplete Multi-Modality Data
• Shared Latent Structures Between Imaging Features and Biomarkers in Early Stages of Alzheimer's Disease
• Predicting Nucleus Basalis of Meynert Volume from Compartmental Brain Segmentations
• Multi-modal Neuroimaging Data Fusion via Latent Space Learning for Alzheimer's Disease Diagnosis
• Transfer Learning for Task Adaptation of Brain Lesion Assessment and Prediction of Brain Abnormalities Progression/Regression Using Irregularity Age Map in Brain MRI
• Multi-View Brain Network Prediction From a Source View Using Sample Selection via CCA-based Multi-Kernel Connectomic Manifold Learning
• Predicting Emotional Intelligence Scores From Multi-Session Functional Brain Connectomes
• Predictive Modeling of Longitudinal Data for Alzheimer's Disease Diagnosis Using RNNs
• Towards Continuous Health Diagnosis from Faces with Deep Learning
• XmoNet: A Fully Convolutional Network for Cross-Modality MR Image Inference
• 3D Convolutional Neural Network and Stacked Bidirectional Recurrent Neural Network for Alzheimer's Disease Diagnosis
• Generative Adversarial Training for MRA Image Synthesis Using Multi-Contrast MRI
• Diffusion MRI Spatial Super-Resolution Using Generative Adversarialv Networks
• Prediction to Atrial Fibrillation Using Deep Convolutional Neural Networks.

This book constitutes the refereed proceedings of the First International Workshop on PRedictive Intelligence in MEdicine, PRIME 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 20 full papers presented were carefully reviewed and selected from 23 submissions. The main aim of the workshop is to propel the advent of predictive models in a broad sense, with application to medical data. Particularly, the workshop will admit papers describing new cutting-edge predictive models and methods that solve challenging problems in the medical field.

• Brain lesion image analysis.-Brain tumor image segmentation.- Ischemic stroke lesion image segmentation.- Grand challenge on MR brain segmentation.- Computational precision medicine.- Stroke workshop on imaging and treatment challenges.
• .
• (source: Nielsen Book Data)

This two-volume set LNCS 11383 and 11384 constitutes revised selected papers from the 4th International MICCAI Brainlesion Workshop, BrainLes 2018, as well as the International Multimodal Brain Tumor Segmentation, BraTS, Ischemic Stroke Lesion Segmentation, ISLES, MR Brain Image Segmentation, MRBrainS18, Computational Precision Medicine, CPM, and Stroke Workshop on Imaging and Treatment Challenges, SWITCH, which were held jointly at the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI, in Granada, Spain, in September 2018. The 92 papers presented in this volume were carefully reviewed and selected from 95 submissions. They were organized in topical sections named: brain lesion image analysis; brain tumor image segmentation; ischemic stroke lesion image segmentation; grand challenge on MR brain segmentation; computational precision medicine; stroke workshop on imaging and treatment challenges.
(source: Nielsen Book Data)

• Brain lesion image analysis.-Brain tumor image segmentation.- Ischemic stroke lesion image segmentation.- Grand challenge on MR brain segmentation.- Computational precision medicine.- Stroke workshop on imaging and treatment challenges.
• .
• (source: Nielsen Book Data)

This two-volume set LNCS 11383 and 11384 constitutes revised selected papers from the 4th International MICCAI Brainlesion Workshop, BrainLes 2018, as well as the International Multimodal Brain Tumor Segmentation, BraTS, Ischemic Stroke Lesion Segmentation, ISLES, MR Brain Image Segmentation, MRBrainS18, Computational Precision Medicine, CPM, and Stroke Workshop on Imaging and Treatment Challenges, SWITCH, which were held jointly at the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI, in Granada, Spain, in September 2018. The 92 papers presented in this volume were carefully reviewed and selected from 95 submissions. They were organized in topical sections named: brain lesion image analysis; brain tumor image segmentation; ischemic stroke lesion image segmentation; grand challenge on MR brain segmentation; computational precision medicine; stroke workshop on imaging and treatment challenges.
(source: Nielsen Book Data)

• Automated Recognition of Erector Spinae Muscles and Their Skeletal Attachment Region via Deep Learning in Torso CT Images
• Fully automatic teeth segmentation in adult OPG images
• Fully Automatic Planning of Total Shoulder Arthroplasty without Segmentation: A Deep Learning Based Approach
• Deep Volumetric Shape Learning for Semantic Segmentation of the Hip Joint from 3D MR Images
• Pelvis segmentation using multi-pass U-net and iterative shape estimation
• Bone Adaptation as Level Set Motion
• Landmark Localisation in Radiographs Using Weighted Heatmap Displacement Voting
• Perthes Disease Classification Using Shape and Appearance Modelling
• Deep Learning Based Rib Centerline Extraction and Labeling
• Automatic Wrist Fracture Detection From Posteroanterior and Lateral Radiographs: A Deep Learning-Based Approach
• Bone Reconstruction and Depth Control During Laser Ablation
• Automated Dynamic 3D Ultrasound Assessment of Developmental Dysplasia of the Infant Hip
• Automated Measurement of Pelvic Incidence from X-Ray Images.

This book constitutes the refereed proceedings of the 6th International Workshop on Computational Methods and Clinical Applications for Musculoskeletal Imaging, MSKI 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 13 workshop papers were carefully reviewed and selected for inclusion in this volume. Topics of interest include all major aspects of musculoskeletal imaging, for example: clinical applications of musculoskeletal computational imaging; computer-aided detection and diagnosis of conditions of the bones, muscles and joints; image-guided musculoskeletal surgery and interventions; image-based assessment and monitoring of surgical and pharmacological treatment; segmentation, registration, detection, localization and visualization of the musculoskeletal anatomy; statistical and geometrical modeling of the musculoskeletal shape and appearance; image-based microstructural characterization of musculoskeletal tissue; novel techniques for musculoskeletal imaging.

• Medical imaging.- Digital pathology.- E-health.- Motor analysis and biosignals.
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This book constitutes the refereed proceedings of the First International SIPAIM Workshop on Processing and Analysis of Biomedical Information, SaMBa 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 14 full papers presented were carefully reviewed and selected for inclusion in this volume. The papers cover the following topics: Medical imaging, Digital pathology, E-health, and Motor analysis and biosignals. The purpose was to present success stories of science, research and innovation stemming from Latin American and to encourage the formation of international academic networks in biomed- ical research with a strong component in medical information processing.
(source: Nielsen Book Data)

• Image Denoising.- Image Registration and Matching.- Image Classification and Detection.- Brain Image Analysis.- Retinal Image Analysis.
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This book constitutes the refereed proceedings of the 4th International Workshop on Patch-Based Techniques in Medical Images, Patch-MI 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 15 full papers presented were carefully reviewed and selected from 17 submissions. The papers are organized in the following topical sections: Image Denoising Image Registration and Matching, Image Classification and Detection, Brain Image Analysis, and Retinal Image Analysis.
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• Part I Diffusion MRI Signal Acquisition and Processing Strategies: Towards Optimal Sampling in Diffusion MRI: H. Knutsson.- Joint Image Reconstruction and Phase Corruption Maps Estimation in Multi-Shot Echo Planar Imaging: I. Rabanillo-Viloria et al.- Return-to_Axis Probability Calculation from Single-Shell Acquisitions: S. Aja-Fernandez et al.- A Novel Spatial-Angular Domain Regularisation Approach for Restoration of Diffusion MRI: A. Mella et al.- Dmipy, a Diffusion Microstructure Imaging Toolbox in Python to Improve Research Reproducibility: A. Alimi et al.- Tissue Segmentation Using Sparse Non-Negative Matrix Factorization of Spherical Mean Diffusion MRI Data: P. Sun et al.- A Closed-Form Solution of Rotation Invariant Spherical Harmonic Features in Diffusion MRI: M. Zucchelli et al.- Orientation-Dispersed Apparent Axon Diameter via Multi-Stage Spherical Mean Optimization: M. Pizzolato et al.-
• Part II Machine Learning for Diffusion MRI: Crurent Applications and Future Promises of Machine Learning in Diffusion MRI: D. Ravi et al.- q-Space Learning with Synthesized Training Data: C. Ye et al.- Graph-Based Deep Learning for Prediction of Longitudinal Infant Diffusion MRI Data: J. Kim et al.- Supervised Classification of White Matter Fibers Based on Neighborhood Fiber Orientation Distributions Using an Ensemble of Neural Networks: D. Ugurlu et al.-
• Part III Diffusion MRI Signal Harmonisation: Challenges and Opportunities in dMRI Data Harmonization: A.H. Zhu et al.- Spherical Harmonic Residual Network for Diffusion Signal Harmonization: S. Koppers et al.- Longitudinal Harmonization for Improving Tractography in Baby Diffusion MRI: K. M. Huynh et al.- Inter-Scanner Harmonization of High Angular Resolution DW-MRI Using Null Space Deep Learning: V. Nath et al.- Effects of Diffusion MRI Model and Harmonization on the Consistency of Findings in an International Multi-Cohort HIV Neuroimaging Study: T.M. Nir et al.- Multi-Shell Diffusion MRI Harmonisation and enhancement Challenge (MUSHAC): Progress and Results: L. Ning et al.- Part IV Diffusion MRI Outside the Brain and Clinical Applications: Diffusion MRI Outside the Brain: R.G. Nunes et al.- A Framework for Calculating Time-Efficient Diffusion MRI Protocols for Anisotropic IVIM and an Application in the Placenta: P.J. Slator et al.- Spatial Characterisation of Fibre Response Functions for Spherical Deconvolution in Multiple Sclerosis: C. Tur et al.- Edge Weights and Network Properties in Multiple Sclerosis: E. Powell et al.- Part V Tractography and Connectivity Mapping: Measures of Tractography Convergence: D.C. Moyer et al.- Brain Connectivity Measures via Direct Sub-Finslerian Front Propagation on the 5D Sphere Bundle of Positions and Directions: J. Portegies et al.- Inference of an Extended Short Fiber Bundle Atlas Using Sulcus-Based Constraints for a Diffeomorphic Inter-Subject Alignment: N.L. Avila et al.- Resolving the Crossing/Kissing Fiber Ambiguity Using Functionally Informed COMMIT: M. Frigo et al.- Voxel-Wise Clustering of Tractography Data for Building Atlases of Local Fiber Geometry: I. Brusini et al.- Obtaining Representative Core Streamlines for White Matter Tractometry of the Human Brain: M. Chamberland et al.- Improving Graph-Based Tractography Plausibility Using Microstructure Information: M. Battocchio et al.- Deterministic Group Tractography with Local Uncertainty Quantification: A.N. Holm et al.- Index.
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This volume gathers papers presented at the Workshop on Computational Diffusion MRI (CDMRI'18), which was held under the auspices of the International Conference on Medical Image Computing and Computer Assisted Intervention in Granada, Spain on September 20, 2018. It presents the latest developments in the highly active and rapidly growing field of diffusion MRI. The reader will find papers on a broad range of topics, from the mathematical foundations of the diffusion process and signal generation, to new computational methods and estimation techniques for the in-vivo recovery of microstructural and connectivity features, as well as harmonisation and frontline applications in research and clinical practice. The respective papers constitute invited works from high-profile researchers with a specific focus on three topics that are now gaining momentum within the diffusion MRI community: i) machine learning for diffusion MRI; ii) diffusion MRI outside the brain (e.g. in the placenta); and iii) diffusion MRI for multimodal imaging. The book shares new perspectives on the latest research challenges for those currently working in the field, but also offers a valuable starting point for anyone interested in learning computational techniques in diffusion MRI. It includes rigorous mathematical derivations, a wealth of full-colour visualisations, and clinically relevant results. As such, it will be of interest to researchers and practitioners in the fields of computer science, MRI physics and applied mathematics alike.
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• Intro
• Preface
• Organization
• Contents
• Shape Applications/Validation/Software
• Deformetrica 4: An Open-Source Software for Statistical Shape Analysis
• 1 Introduction
• 2 Theoretical Background
• 2.1 Control-Points-Based LDDMM: Constructing Diffeomorphisms
• 2.2 Diffeomorphic Action on Shapes: Deforming Meshes or Images
• 2.3 Shape Attachments: Evaluting Deformation Residuals
• 2.4 A Glimpse at Optimization
• 3 Performances
• 4 Deformetrica Applications
• 4.1 Atlas and Registration
• 4.2 Bayesian Atlas
• 4.3 Geodesic Regression
• 4.4 Parallel Transport in Shape Analysis

• 5 Conclusion
• References
• On the Evaluation and Validation of Off-the-Shelf Statistical Shape Modeling Tools: A Clinical Application
• 1 Introduction
• 2 Methods
• 2.1 Statistical Shape Models
• 2.2 SSM Tools
• 2.3 Evaluation Methodology
• 2.4 Validation Methodology
• 3 Results
• 3.1 Experimental Setup
• 3.2 Shape Models Evaluation
• 3.3 Shape Models Validation
• 4 Conclusion
• References
• Characterizing Anatomical Variability and Alzheimer's Disease Related Cortical Thinning in the Medial Temporal Lobe Using Graph-Based Groupwise Registration and Point Set Geodesic Shooting

• Abstract
• 1 Introduction
• 2 Materials and Method
• 2.1 Dataset
• 2.2 Construction of Statistical Models of Anatomical Variants of the PRC
• 2.2.1 Template Construction Using Graph-Based Groupwise Registration
• 2.2.2 Quantifying Shape Variability Using Pointset Geodesic Shooting
• 2.3 Fitting the Templates to a New Target Image
• 3 Experiments and Results
• 3.1 Statistical Shape Models
• 3.2 AD-Related Cortical Thinning
• 3.3 Effect of AD on MTL Shape
• 4 Conclusion
• Acknowledgements
• References
• Interpretable Spiculation Quantification for Lung Cancer Screening

• 1 Introduction
• 2 Method
• 2.1 Conformal Mappings and Area Distortion
• 2.2 Spiculation Quantification Pipeline
• 2.3 Spiculation Score
• 2.4 Spiculation Classification and Malignancy Prediction
• 3 Results
• 3.1 Spiculation Classification
• 3.2 Malignancy Prediction
• 4 Conclusion and Future Work
• References
• Shape and Facet Analyses of Alveolar Airspaces of the Lung
• 1 Introduction
• 2 Methods
• 2.1 Sample Preparation, Data Acquisition and Reconstruction
• 2.2 Segmentation, Partition Creation and Processing
• 2.3 Quantities per Alveoli and Histograms

• 2.4 Facet Analysis of Alveoli
• 2.5 Shape Analysis of Alveoli
• 2.6 Processing Dependencies, Source Code and Reproduction
• 3 Results
• 3.1 Morphometric Data of Individual Alveoli
• 3.2 Angle Distribution Between Interalveolar Septa
• 3.3 Distribution of the Number of Neighboring Alveoli
• 3.4 Shape of Individual Alveolar Airspaces
• 4 Discussion
• 5 Conclusion
• A Catalogue
• B Video
• References
• SlicerSALT: Shape AnaLysis Toolbox
• 1 Introduction
• 2 Available Extensions
• 2.1 Home
• 2.2 Data Importer
• 2.3 SPHARM-PDM

This book constitutes the proceedings of the Workshop on Shape in Medical Imaging, ShapeMI 2018, held in conjunction with the 21st International Conference on Medical Image Computing, MICCAI 2018, in Granada, Spain, in September 2018. The 26 full papers and 2 short papers presented were carefully reviewed and selected for inclusion in this volume. The papers discuss novel approaches and applications in shape and geometry processing and their use in research and clinical studies and explore novel, cutting-edge theoretical methods and their usefulness for medical applications, e.g., from the fields of geometric learning or spectral shape analysis. .
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• Towards Ultra-high Resolution 3D Reconstruction of a Whole Rat Brain from 3D-PLI Data.- FOD-based Registration for Susceptibility Distortion Correction in Connectome Imaging.- GIFE: Efficient and Robust Group-wise Isometric Fiber Embedding.- Multi-Modal Brain Tensor Factorization: Preliminary Results with AD Patients.- Intact Connectional Morphometricity Learning Using Multi-View Morphological Brain Networks with Application to Autism Spectrum Disorder.- Neonatal Morphometric Similarity Networks Predict Atypical Brain Development Associated with Preterm Birth.- Heritability Estimation of Reliable Connectomic Features.- Topological Data Analysis of Functional MRI Connectivity in Time and Space Domains.- Riemannian Regression and Classification Models of Brain Networks Applied to Autism.- Defining Patient Specific Functional Parcellations in Lesional Cohorts via Markov Random Fields.- Data-Specific Feature Selection Method Identification for Most Reproducible Connectomic Feature Discovery Fingerprinting Brain States.- Towards Effective Functional Connectome Fingerprinting.- Connectivity-Driven Brain Parcellation via Consensus Clustering.- GRAND: Unbiased Connectome Atlas of Brain Network by Groupwise Graph Shrinkage and Network Diffusion.- Structural Subnetwork Evolution Across the Lifespan: Rich-club, Feeder, Seeder.
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This book constitutes the refereed proceedings of the Second International Workshop on Connectomics in NeuroImaging, CNI 2018, held in conjunction with MICCAI 2018 in Granada, Spain, in September 2018. The 15 full papers presented were carefully reviewed and selected from 20 submissions. The papers deal with new advancements in network construction, analysis, and visualization techniques in connectomics and their use in clinical diagnosis and group comparison studies as well as in various neuroimaging applications.
(source: Nielsen Book Data)

• Improving Accuracy of Nuclei Segmentation by Reducing Histological Image Variability.- Multi-Resolution Networks for Semantic Segmentation in Whole Slide Images.- Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network.- Construction of a Generative Model of H&E Stained Pathology Images of Pancreas Tumors Conditioned by a Voxel Value of MRI Image.- Accurate 3D reconstruction of a whole pancreatic cancer tumor from pathology images with different stains.- Role of Task Complexity and Training in Crowdsourced Image Annotation.- Capturing global spatial context for accurate cell classification in skin cancer histology.- Exploiting Multiple Color Representations to Improve Colon Cancer Detection in Whole Slide H&E Stains.- Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection.- Evaluating Out-of-the-box Methods for the Classification of Hematopoietic Cells in Images of Stained Bone Marrow.- DeepCerv: Deep neural network for segmentation free robust cervical cell classification.- Whole slide image registration for the study of tumor heterogeneity.- Modality Conversion from Pathological Image to Ultrasonic Image Using Convolutional Neural Network.- Structure instance segmentation in renal tissue: a case study on tubular immune cell detection.- Cellular Community Detection for Tissue Phenotyping in Histology Images.- Automatic Detection of Tumor Budding in Colorectal Carcinoma with Deep Learning.- Significance of Hyperparameter Optimization for Metastasis Detection in Breast Histology Images.- Image Magnification Regression Using DenseNet for Exploiting Histopathology Open Access Content.- Uncertainty Driven Pooling Network for Microvessel Segmentation in Routine Histology Images.- Ocular Structures Segmentation from Multi-sequences MRI using 3D Unet with Fully Connected CRFs.- Classification of Findings with Localized Lesions in Fundoscopic Images using a Regionally Guided CNN.- Segmentation of Corneal Nerves Using a U-Net-based Convolutional Neural Network.- Automatic Pigmentation Grading of the Trabecular Meshwork in Gonioscopic Images.- Large Receptive Field Fully Convolutional Network for Semantic Segmentation of Retinal Vasculature in Fundus Images.- Explaining Convolutional Neural Networks for Area Estimation of Choroidal Neovascularization via Genetic Programming.- Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images using Bayesian Deep Learning.- cGAN-based lacquer cracks segmentation in ICGA image.- Localizing Optic Disc and Cup for Glaucoma Screening via Deep Object Detection Networks.- Fundus Image Quality-guided Diabetic Retinopathy Grading.- DeepDisc: Optic Disc Segmentation based on Atrous Convolution and Spatial Pyramid Pooling.- Large-scale Left and Right Eye Classification in Retinal Images.- Automatic Segmentation of Cortex and Nucleus in Anterior Segment OCT Images.- Local Estimation of the Degree of Optic Disc Swelling from Color Fundus Photography.- Visual Field based Automatic Diagnosis of Glaucoma Using Deep Convolutional Neural Network.- Towards standardization of retinal vascular measurements: on the effect of image centering.- Feasibility study of Subfoveal Choroidal Thickness Changes in Spectral-Domain Optical Coherence Tomography Measurements of Macular Telangiectasia Type 2.- Segmentation of retinal layers in OCT images of the mouse eye utilizing polarization contrast.- Glaucoma Diagnosis from Eye Fundus Images Based on Deep Morphometric Feature Estimation.- 2D Modeling and Correction of Fan-beam Scan Geometry in OCT.- A Bottom-up Saliency Estimation Approach for Neonatal Retinal Images.
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This book constitutes the refereed joint proceedings of the First International Workshop on Computational Pathology, COMPAY 2018, and the 5th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 19 full papers (out of 25 submissions) presented at COMPAY 2018 and the 21 full papers (out of 31 submissions) presented at OMIA 2018 were carefully reviewed and selected. The COMPAY papers focus on artificial intelligence and deep learning. The OMIA papers cover various topics in the field of ophthalmic image analysis.
(source: Nielsen Book Data)

• Blood-flow estimation in the hepatic arteries based on 3D/2D angiography registration.- Automated quantification of blood flow velocity from time-resolved CT angiography.- Multiple device segmentation for fluoroscopic imaging using multi-task learning.- Segmentation of the Aorta Using Active Contours with Histogram-Based Descriptors.- Layer Separation in X-ray Angiograms for Vessel Enhancement with Fully Convolutional Network.- Generation of a HER2 breast cancer gold-standard using supervised learning from multiple experts.- Deep Learning-based Detection and Segmentation for BVS Struts in IVOCT Images.- Towards Automatic Measurement of Type B Aortic Dissection Parameters.- Prediction of FFR from IVUS Images using Machine Learning.- Deep Learning Retinal Vessel Segmentation From a Single Annotated Example: An Application of Cyclic Generative Adversarial Neural Networks.- An Efficient and Comprehensive Labeling Tool for Large-scale Annotation of Fundus Images.- Crowd disagreement about medical images is informative.- Imperfect Segmentation Labels: How Much Do They Matter?.- Crowdsourcing annotation of surgical instruments in videos of cataract surgery.- Four-dimensional ASL MR angiography phantoms with noise learned by neural styling.- Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans.- Capsule Networks against Medical Imaging Data Challenges.- Fully Automatic Segmentation of Coronary Arteries based on Deep Neural Network in Intravascular Ultrasound Images.- Weakly-Supervised Learning for Tool Localization in Laparoscopic Videos.- Radiology Objects in COntext (ROCO).- Improving out-of-sample prediction of quality of MRIQC.
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This book constitutes the refereed joint proceedings of the 7th Joint International Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2018, and the Third International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2018, held in conjunction with the 21th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 9 full papers presented at CVII-STENT 2017 and the 12 full papers presented at LABELS 2017 were carefully reviewed and selected. The CVII-STENT papers feature the state of the art in imaging, treatment, and computer-assisted intervention in the field of endovascular interventions. The LABELS papers present a variety of approaches for dealing with few labels, from transfer learning to crowdsourcing.
(source: Nielsen Book Data)