Medical image computing and computer assisted intervention -- MICCAI 2019 : 22nd International Conference, Shenzhen, China, October 13-17, 2019, proceedings
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書誌事項
Medical image computing and computer assisted intervention -- MICCAI 2019 : 22nd International Conference, Shenzhen, China, October 13-17, 2019, proceedings
（Lecture notes in computer science, 11765 . LNCS sublibrary ; SL 6 . Image processing,
Springer, c2019
- pt. 2
- タイトル別名
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MICCAI 2019
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注記
Other editors: Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan
Includes bibliographical references and author index
内容説明・目次
内容説明
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.
目次
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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