Del 11765 i serien Lecture Notes in Computer Science

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II

Häftad, Engelska, 2019

AvDinggang Shen,Tianming Liu,Terry M. Peters,Lawrence H. Staib,Caroline Essert,Sean Zhou,Pew-Thian Yap,Ali Khan

1 439 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt för medlemmar vid köp för minst 249 kr.

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.

Produktinformation

Utgivningsdatum2019-10-18
Mått155 x 235 x 50 mm
Vikt1 338 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor874
FörlagSpringer Nature Switzerland AG
ISBN9783030322441

Tillhör följande kategorier

Tillämpad datateknik inom Data och IT
Systemvetenskap och AI inom Data och IT
Artificiell intelligens inom Data och IT

Image Segmentation.- Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation.- Comparative Evaluation of Hand-Engineered and Deep-Learned Features for Neonatal Hip Bone Segmentation in Ultrasound.- Unsupervised Quality Control of Image Segmentation based on Bayesian Learning.- One Network To Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation.- 'Project & Excite' Modules for Segmentation of Volumetric Medical Scans.- Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation.- Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation.- Extreme Points Derived Confidence Map as a Cue For Class-Agnostic Segmentation Using Deep Neural Network.- Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation.- Instance Segmentation from Volumetric Biomedical Images without Voxel-Wise Labeling.- Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice.- Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation.- HD-Net: Hybrid Discriminative Network for Prostate Segmentation in MR Images.- PHiSeg: Capturing Uncertainty in Medical Image Segmentation.- Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data.- Supervised Uncertainty Quantification for Segmentation with Multiple Annotations.- 3D Tiled Convolution for Effective Segmentation of Volumetric Medical Images.- Hyper-Pairing Network for Multi-Phase Pancreatic Ductal Adenocarcinoma Segmentation.- Statistical intensity- and shape-modeling to automate cerebrovascular segmentation from TOF-MRA data.- Segmentation of Vessels in Ultra High Frequency Ultrasound Sequences using Contextual Memory.- Accurate Esophageal Gross Tumor Volume Segmentation in PET/CT using Two-Stream Chained 3D Deep Network Fusion.- Mixed-Supervised Dual-Network for Medical Image Segmentation.- Fully Automated Pancreas Segmentation with Two-stage 3D Convolutional Neural Networks.- Globally Guided Progressive Fusion Network for 3D Pancreas Segmentation.- Automatic Segmentation of Muscle Tissue and Inter-muscular Fat in Thigh and Calf MRI Images.- Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation.- Radiomics-guided GAN for Segmentation of Liver Tumor without Contrast Agents.- Liver Segmentation in Magnetic Resonance Imaging via Mean Shape Fitting with Fully Convolutional Neural Networks.- Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation.- Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss.- Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation.- Collaborative Multi-agent Learning for MR Knee Articular Cartilage Segmentation.- 3D U2-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation.- Impact of Adversarial Examples on Deep Learning Segmentation Models.- Multi-Resolution Path CNN with Deep Supervision for Intervertebral Disc Localization and Segmentation.- Automatic paraspinal muscle segmentation in patients with lumbar pathology using deep convolutional neural network.- Constrained Domain Adaptation for Segmentation.- Image Registration.-Image-and-Spatial Transformer Networks for Structure-Guided Image Registration.- Probabilistic Multilayer Regularization Network for Unsupervised 3D Brain Image Registration.- A deep learning approach to MR-less spatial normalization for tau PET images.- TopAwaRe: Topology-Aware Registration.- Multimodal Data Registration for Brain Structural Association Networks.- Dual-Stream Pyramid Registration Network.- A Cooperative Autoencoder for Population-Based Regularization of CNN Image Registration.- Conditional Segmentation in Lieu of Image Registration.- On the applicability of registration uncertainty.- DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation.- Linear Time Invariant Model based Motion Correction (LiMo-Moco) of Dynamic Radial Contrast Enhanced MRI.- Incompressible image registration using divergence-conforming B-splines.- Cardiovascular Imaging.- Direct Quantification for Coronary Artery Stenosis Using Multiview Learning.- Bayesian Optimization on Large Graphs via a Graph Convolutional Generative Model: Application in Cardiac Model Personalization.- Discriminative Coronary Artery Tracking via 3D CNN in Cardiac CT Angiography.- Multi-modality Whole-Heart and Great Vessel Segmentation in Congenital Heart Disease using Deep Neural Networks and Graph Matching.- Harmonic Balance Techniques in Cardiovascular Fluid Mechanics.- Deep learning within a priori temporal feature spaces for large-scale dynamic MR image reconstruction: Application to 5-D cardiac MR Multitasking.- k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-temporal Correlations.- Model-based reconstruction for highly accelerated first-pass perfusion cardiac MRI.- Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view images.- Right Ventricle Segmentation in Short-Axis MRI Using A Shape Constrained Dense Connected U-net.- Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction.- A Fine-Grain Error Map Prediction and Segmentation Quality Assessment Framework for Whole-Heart Segmentation.- Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priors.- Curriculum semi-supervised segmentation.- A Multi-modal Network for Cardiomyopathy Death Risk Prediction with CMR Images and Clinical Information.-3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata.- Discriminative Consistent Domain Generation for Semi-supervised Learning.- Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation.- MSU-Net: Multiscale Statistical U-Net for Real-time 3D Cardiac MRI Video Segmentation.- The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GAN.- Cardiac MRI Segmentation with Strong Anatomical Guarantees.- Decompose-and-Integrate Learning for Multi-class Segmentation in Medical Images.- Missing Slice Imputation in Population CMR Imaging via Conditional Generative Adversarial Nets.- Unsupervised Standard Plane Synthesis in Population Cine MRI via Cycle-Consistent Adversarial Networks.- Data Efficient Unsupervised Domain Adaptation for Cross-Modality Image Segmentation.- Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation.- Echocardiography View Classification Using Quality Transfer Star Generative Adversarial Networks.- Dual-view Joint Estimation of Left Ventricular Ejection Fraction with Uncertainty Modelling in Echocardiograms.- Frame Rate Up-Conversion in Echocardiography Using a Conditioned Variational Autoencoder and Generative Adversarial Model.- Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images.- DeepAAA: clinically applicable and generalizable detection of abdominal aortic aneurysm using deep learning.- Texture-based classification of significant stenosis in CCTA multi-view images of coronary arteries.- Fourier Spectral Dynamic Data Assimilation: Interlacing CFD with 4D flow MRI.- Quality Control-Driven Image Segmentation Towards Reliable Automatic Image Analysis in Large-Scale Cardiovascular Magnetic Resonance Aortic Cine Imaging.- HFA-Net: 3D Cardiovascular Image Segmentation with Asymmetrical Pooling and Content-Aware Fusion.- Spectral CT based training dataset generation and augmentation for conventional CT vascular segmentation.- Context-Aware Inductive Bias Learning for Vessel Border Detection in Multi-modal Intracoronary Imaging.- Growth, Development, Atrophy and Progression.- Neural parameters estimation for brain tumor growth modeling.- Learning-Guided Infinite Network Atlas Selection for Predicting Longitudinal Brain Network Evolution from a Single Observation.- Deep Probabilistic Modeling of Glioma Growth.- Surface-Volume Consistent Construction of Longitudinal Atlases for the Early Developing Brains.- Variational Autoencoder for Regression: Application to Brain Aging Analysis.- Early Development of Infant Brain Complex Network.- Revealing Developmental Regionalization of Infant Cerebral Cortex Based on Multiple Cortical Properties.- Continually Modeling Alzheimer's Disease Progression via Deep Multi-Order Preserving Weight Consolidation.- Disease Knowledge Transfer across Neurodegenerative Diseases.

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Del 11765 i serien Lecture Notes in Computer Science

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II

Häftad, Engelska, 2019

AvDinggang Shen,Tianming Liu,Terry M. Peters,Lawrence H. Staib,Caroline Essert,Sean Zhou,Pew-Thian Yap,Ali Khan

1 439 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt för medlemmar vid köp för minst 249 kr.

Produktinformation

Utgivningsdatum2019-10-18
Mått155 x 235 x 50 mm
Vikt1 338 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor874
FörlagSpringer Nature Switzerland AG
ISBN9783030322441

Tillhör följande kategorier

Tillämpad datateknik inom Data och IT
Systemvetenskap och AI inom Data och IT
Artificiell intelligens inom Data och IT

Image Segmentation.- Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation.- Comparative Evaluation of Hand-Engineered and Deep-Learned Features for Neonatal Hip Bone Segmentation in Ultrasound.- Unsupervised Quality Control of Image Segmentation based on Bayesian Learning.- One Network To Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation.- 'Project & Excite' Modules for Segmentation of Volumetric Medical Scans.- Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation.- Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation.- Extreme Points Derived Confidence Map as a Cue For Class-Agnostic Segmentation Using Deep Neural Network.- Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation.- Instance Segmentation from Volumetric Biomedical Images without Voxel-Wise Labeling.- Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice.- Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation.- HD-Net: Hybrid Discriminative Network for Prostate Segmentation in MR Images.- PHiSeg: Capturing Uncertainty in Medical Image Segmentation.- Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data.- Supervised Uncertainty Quantification for Segmentation with Multiple Annotations.- 3D Tiled Convolution for Effective Segmentation of Volumetric Medical Images.- Hyper-Pairing Network for Multi-Phase Pancreatic Ductal Adenocarcinoma Segmentation.- Statistical intensity- and shape-modeling to automate cerebrovascular segmentation from TOF-MRA data.- Segmentation of Vessels in Ultra High Frequency Ultrasound Sequences using Contextual Memory.- Accurate Esophageal Gross Tumor Volume Segmentation in PET/CT using Two-Stream Chained 3D Deep Network Fusion.- Mixed-Supervised Dual-Network for Medical Image Segmentation.- Fully Automated Pancreas Segmentation with Two-stage 3D Convolutional Neural Networks.- Globally Guided Progressive Fusion Network for 3D Pancreas Segmentation.- Automatic Segmentation of Muscle Tissue and Inter-muscular Fat in Thigh and Calf MRI Images.- Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation.- Radiomics-guided GAN for Segmentation of Liver Tumor without Contrast Agents.- Liver Segmentation in Magnetic Resonance Imaging via Mean Shape Fitting with Fully Convolutional Neural Networks.- Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation.- Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss.- Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation.- Collaborative Multi-agent Learning for MR Knee Articular Cartilage Segmentation.- 3D U2-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation.- Impact of Adversarial Examples on Deep Learning Segmentation Models.- Multi-Resolution Path CNN with Deep Supervision for Intervertebral Disc Localization and Segmentation.- Automatic paraspinal muscle segmentation in patients with lumbar pathology using deep convolutional neural network.- Constrained Domain Adaptation for Segmentation.- Image Registration.-Image-and-Spatial Transformer Networks for Structure-Guided Image Registration.- Probabilistic Multilayer Regularization Network for Unsupervised 3D Brain Image Registration.- A deep learning approach to MR-less spatial normalization for tau PET images.- TopAwaRe: Topology-Aware Registration.- Multimodal Data Registration for Brain Structural Association Networks.- Dual-Stream Pyramid Registration Network.- A Cooperative Autoencoder for Population-Based Regularization of CNN Image Registration.- Conditional Segmentation in Lieu of Image Registration.- On the applicability of registration uncertainty.- DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation.- Linear Time Invariant Model based Motion Correction (LiMo-Moco) of Dynamic Radial Contrast Enhanced MRI.- Incompressible image registration using divergence-conforming B-splines.- Cardiovascular Imaging.- Direct Quantification for Coronary Artery Stenosis Using Multiview Learning.- Bayesian Optimization on Large Graphs via a Graph Convolutional Generative Model: Application in Cardiac Model Personalization.- Discriminative Coronary Artery Tracking via 3D CNN in Cardiac CT Angiography.- Multi-modality Whole-Heart and Great Vessel Segmentation in Congenital Heart Disease using Deep Neural Networks and Graph Matching.- Harmonic Balance Techniques in Cardiovascular Fluid Mechanics.- Deep learning within a priori temporal feature spaces for large-scale dynamic MR image reconstruction: Application to 5-D cardiac MR Multitasking.- k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-temporal Correlations.- Model-based reconstruction for highly accelerated first-pass perfusion cardiac MRI.- Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view images.- Right Ventricle Segmentation in Short-Axis MRI Using A Shape Constrained Dense Connected U-net.- Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction.- A Fine-Grain Error Map Prediction and Segmentation Quality Assessment Framework for Whole-Heart Segmentation.- Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priors.- Curriculum semi-supervised segmentation.- A Multi-modal Network for Cardiomyopathy Death Risk Prediction with CMR Images and Clinical Information.-3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata.- Discriminative Consistent Domain Generation for Semi-supervised Learning.- Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation.- MSU-Net: Multiscale Statistical U-Net for Real-time 3D Cardiac MRI Video Segmentation.- The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GAN.- Cardiac MRI Segmentation with Strong Anatomical Guarantees.- Decompose-and-Integrate Learning for Multi-class Segmentation in Medical Images.- Missing Slice Imputation in Population CMR Imaging via Conditional Generative Adversarial Nets.- Unsupervised Standard Plane Synthesis in Population Cine MRI via Cycle-Consistent Adversarial Networks.- Data Efficient Unsupervised Domain Adaptation for Cross-Modality Image Segmentation.- Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation.- Echocardiography View Classification Using Quality Transfer Star Generative Adversarial Networks.- Dual-view Joint Estimation of Left Ventricular Ejection Fraction with Uncertainty Modelling in Echocardiograms.- Frame Rate Up-Conversion in Echocardiography Using a Conditioned Variational Autoencoder and Generative Adversarial Model.- Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images.- DeepAAA: clinically applicable and generalizable detection of abdominal aortic aneurysm using deep learning.- Texture-based classification of significant stenosis in CCTA multi-view images of coronary arteries.- Fourier Spectral Dynamic Data Assimilation: Interlacing CFD with 4D flow MRI.- Quality Control-Driven Image Segmentation Towards Reliable Automatic Image Analysis in Large-Scale Cardiovascular Magnetic Resonance Aortic Cine Imaging.- HFA-Net: 3D Cardiovascular Image Segmentation with Asymmetrical Pooling and Content-Aware Fusion.- Spectral CT based training dataset generation and augmentation for conventional CT vascular segmentation.- Context-Aware Inductive Bias Learning for Vessel Border Detection in Multi-modal Intracoronary Imaging.- Growth, Development, Atrophy and Progression.- Neural parameters estimation for brain tumor growth modeling.- Learning-Guided Infinite Network Atlas Selection for Predicting Longitudinal Brain Network Evolution from a Single Observation.- Deep Probabilistic Modeling of Glioma Growth.- Surface-Volume Consistent Construction of Longitudinal Atlases for the Early Developing Brains.- Variational Autoencoder for Regression: Application to Brain Aging Analysis.- Early Development of Infant Brain Complex Network.- Revealing Developmental Regionalization of Infant Cerebral Cortex Based on Multiple Cortical Properties.- Continually Modeling Alzheimer's Disease Progression via Deep Multi-Order Preserving Weight Consolidation.- Disease Knowledge Transfer across Neurodegenerative Diseases.

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Tianming Liu, Dinggang Shen, LUIS IBANEZ, Xiaodong Tao

Häftad, 2011

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Del 11767

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan

Häftad, 2019

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Del 11769

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan

Häftad, 2019

1 439 kr

Multimodal Brain Image Analysis

Pew-Thian Yap, Tianming Liu, Dinggang Shen, Carl-Fredrik Westin, Li Shen

Häftad, 2012

689 kr

Del 11764

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan

Häftad, 2019

1 439 kr

Del 8159

Multimodal Brain Image Analysis

Li Shen, Tianming Liu, Pew-Thian Yap, Heng Huang, Dinggang Shen, Carl-Fredrik Westin

Häftad, 2013

739 kr

Del 11766

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan

Häftad, 2019

1 479 kr

Del 11768

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan

Häftad, 2019

719 kr

Del 10265

Information Processing in Medical Imaging

Marc Niethammer, Martin Styner, Stephen Aylward, Hongtu Zhu, Ipek Oguz, Pew-Thian Yap, Dinggang Shen

Häftad, 2017

739 kr

Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions

Hongen Liao, Cristian A Linte, Ken Masamune, Terry M. Peters, Guayan Zheng

Häftad, 2013

689 kr

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II

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Innehållsförteckning

Du kanske också är intresserad av

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II

Produktinformation

Tillhör följande kategorier

Innehållsförteckning

Du kanske också är intresserad av

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II

Produktinformation

Tillhör följande kategorier

Innehållsförteckning

Du kanske också är intresserad av