Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, Dinggang Shen; Tianming Liu; Terry M. Peters; Lawr

Автор: Dinggang Shen; Tianming Liu; Terry M. Peters; Lawr
Название: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
ISBN: 303032253X ISBN-13(EAN): 9783030322533
Издательство: Springer
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Computer Assisted Interventions.- Robust Cochlear Modiolar Axis Detection in CT.- Learning to Avoid Poor Images: Towards Task-aware C-arm Cone-beam CT Trajectories.- Optimizing Clearance of Bйzier Spline Trajectories for Minimally-Invasive Surgery.- Direct Visual and Haptic Volume Rendering of Medical Data Sets for an Immersive Exploration in Virtual Reality.- Triplet Feature Learning on Endoscopic Video Manifold for Real-time Gastrointestinal Image Retargeting.- A Novel Endoscopic Navigation System: Simultaneous Endoscope and Radial Ultrasound Probe Tracking Without External Trackers.- An Extremely Fast and Precise Convolutional Neural Network for Recognition and Localization of Cataract Surgical Tools.- Semi-autonomous Robotic Anastomoses of Vaginal Cuffs using Marker Enhanced 3D Imaging and Path Planning.- Augmented Reality "X-Ray Vision" for Laparoscopic Surgery using Optical See-Through Head-Mounted Display.- Interactive Endoscopy: A Next-Generation, Streamlined User Interface for Lung Surgery Navigation.- Non-invasive Assessment of In Vivo Auricular Cartilage by Ultrashort Echo Time (UTE) T2* Mapping.- INN: Inflated Neural Networks for IPMN Diagnosis.- Development of an Multi-objective Optimized Planning Method for Microwave Liver Tumor Ablation.- Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation.- Mask-MCNet: Instance Segmentation in 3D Point Cloud of Intra-oral Scans.- Physics-based Deep Neural Network for Augmented Reality during Liver Surgery.- Detecting Cannabis-Associated Cognitive Impairment using Resting-state fNIRS.- Cross-Domain Conditional Generative Adversarial Networks for Stereoscopic Hyperrealism in Surgical Training.- A Free-view, 3D Gaze-Guided Robotic Scrub Nurse.- Haptic Modes for Multiparameter Control in Robotic Surgery.- Learning to Detect Collisions for Continuum Manipulators without a Prior Model.- Simulation of Balloon-Expandable Coronary Stent Apposition with Plastic Beam Elements.- Virtual Cardiac Surgical Planning through Hemodynamics Simulation and Design Optimization of Fontan Grafts.- 3D Modelling of the residual freezing for renal cryoablation simulation and prediction.- A generative model of hyperelastic strain energy density functions for real-time simulation of brain tissue deformation.- Variational Mandible Shape Completion for Virtual Surgical Planning.- Markerless Image-to-Face Registration for Untethered Augmented Reality in Head and Neck Surgery.- Towards a first mixed-reality first person point of view needle navigation system.- Concept-Centric Visual Turing Tests for Method Validation.- Transferring from ex-vivo to in-vivo: Instrument Localization in 3D Cardiac Ultrasound Using Pyramid-UNet with Hybrid Loss.- A Sparsely Distributed Intra-cardial Ultrasonic Array for Real-time Endocardial Mapping.- FetusMap: Fetal Pose Estimation in 3D Ultrasound.- Agent with Warm Start and Active Termination for Plane Localization in 3D Ultrasound.- Learning and Understanding Deep Spatio-Temporal Representations from Free-Hand Fetal Ultrasound Sweeps.- User guidance for point-of-care echocardiography using multi-task deep neural network.- Integrating 3D Geometry of Organ for Improving Medical Imaging Segmentation.- Estimating Reference Bony Shape Model for Personalized Surgical Reconstruction of Posttraumatic Facial Defects.- A New Approach of Predicting Facial Changes following Orthognathic Surgery using Realistic Lip Sliding Effect.- An Automatic Approach to Reestablish Final Dental Occlusion for 1-Piece Maxillary Orthognathic Surgery.- MIC meets CAI.- A Two-stage Framework for Real-time Guidewire Endpoint Localization.- Investigating the role of VR in a simulation-based medical planning system for coronary interventions.- Learned Full-sampling Reconstruction.- A deep regression model for seed localization in prostate brachytherapy.- Model-Based Surgical Recommendations for Optimal Placement of

Автор: Polina Golland; Nobuhiko Hata; Christian Barillot;
Название: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014
ISBN: 3319104039 ISBN-13(EAN): 9783319104034
Издательство: Springer
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Цена: 89440.00 T
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Описание: The three-volume set LNCS 8673, 8674, and 8675 constitutes the refereed proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014, held in Boston, MA, USA, in September 2014.

Автор: Alejandro F. Frangi; Julia A. Schnabel; Christos D
Название: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
ISBN: 303000936X ISBN-13(EAN): 9783030009366
Издательство: Springer
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Цена: 91300.00 T
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Описание: 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.

Автор: Dinggang Shen; Tianming Liu; Terry M. Peters; Lawr
Название: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
ISBN: 3030322475 ISBN-13(EAN): 9783030322472
Издательство: Springer
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Цена: 93160.00 T
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Neuroimage Reconstruction and Synthesis.- Isotropic MRI Super-Resolution Reconstruction with Multi-Scale Gradient Field Prior.- A Two-Stage Multi-Loss Super-Resolution Network For Arterial Spin Labeling Magnetic Resonance Imaging.- Model Learning: Primal Dual Networks for Fast MR imaging.- Model-based Convolutional De-Aliasing Network Learning for Parallel MR Imaging.- Joint Reconstruction of PET + Parallel-MRI in a Bayesian Coupled-Dictionary MRF Framework.- Deep Learning Based Framework for Direct Reconstruction of PET Images.- Nonuniform Variational Network: Deep Learning for Accelerated Nonuniform MR Image Reconstruction.- Reconstruction of Isotropic High-Resolution MR Image from Multiple Anisotropic Scans using Sparse Fidelity Loss and Adversarial Regularization.- Single Image Based Reconstruction of High Field-like MR Images.- Deep Neural Network for QSM Background Field Removal.- RinQ Fingerprinting: Recurrence-informed Quantile Networks for Magnetic Resonance Fingerprinting.- RCA-U-Net: Residual Channel Attention U-Net for Fast Tissue Quantification in Magnetic Resonance Fingerprinting.- GANReDL: Medical Image enhancement using a generative adversarial network with real-order derivative induced loss functions.- Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks.- Semi-Supervised VAE-GAN for Out-of-Sample Detection Applied to MRI Quality Control.- Disease-Image Specific Generative Adversarial Network for Brain Disease Diagnosis with Incomplete Multi-Modal Neuroimages.- Predicting the Evolution of White Matter Hyperintensities in Brain MRI using Generative Adversarial Networks and Irregularity Map.- CoCa-GAN: Common-feature-learning-based Context-aware Generative Adversarial Network for Glioma Grading.- Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression.- Neuroimage Segmentation.- Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation.- 3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI.- Refined-Segmentation R-CNN: A Two-stage Convolutional Neural Network for Punctate White Matter Lesion Segmentation in Preterm Infants.- VoteNet: A Deep Learning Label Fusion Method for Multi-Atlas Segmentation.- Weakly Supervised Brain Lesion Segmentation via Attentional Representation Learning.- Scalable Neural Architecture Search for 3D Medical Image Segmentation.- Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation From Multimodal Unpaired Images.- High Resolution Medical Image Segmentation using Data-swapping Method.- X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies.- Multi-View Semi-supervised 3D Whole Brain Segmentation with a Self-Ensemble Network.- CLCI-Net: Cross-Level Fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke.- Brain Segmentation from k-space with End-to-end Recurrent Attention Network.- Spatial Warping Network for 3D Segmentation of the Hippocampus in MR Images.- CompareNet: Anatomical Segmentation Network with Deep Non-local Label Fusion.- A Joint 3D+2D Fully Convolutional Framework for Subcortical Segmentation.- U-ReSNet: Ultimate coupling of Registration and Segmentation with deep Nets.- Generative adversarial network for segmentation of motion affected neonatal brain MRI.- Interactive deep editing framework for medical image segmentation.- Multiple Sclerosis Lesion Segmentation with Tiramisu and 2.5D Stacked Slices.- Improving Multi-Atlas Segmentation by Convolutional Neural Network Based Patch Error Estimation.- Unsupervised deep learning for Bayesian brain MRI segmentation.- Online atlasing using an iterative centroid.- ARS-Net: Adaptively Rectified Supervision Network for Automated 3D Ultrasound Image Segmentation.- Complete Fetal Head Compounding from Multi-View 3D Ultrasound.- SegNAS3D: Network Architecture Search with Derivative-Free Glo

Автор: Dinggang Shen; Tianming Liu; Terry M. Peters; Lawr
Название: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
ISBN: 3030322254 ISBN-13(EAN): 9783030322250
Издательство: Springer
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Цена: 93160.00 T
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Описание: 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.

Автор: Dinggang Shen; Tianming Liu; Terry M. Peters; Lawr
Название: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
ISBN: 3030322505 ISBN-13(EAN): 9783030322502
Издательство: Springer
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Цена: 46570.00 T
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Shape.- A CNN-Based Framework for Statistical Assessment of Spinal Shape and Curvature in Whole-Body MRI Images of Large Populations.- Exploiting Reliability-guided Aggregation for the Assessment of Curvilinear Structure Tortuosity.- A Surface-theoretic Approach for Statistical Shape Modeling.- Shape Instantiation from A Single 2D Image to 3D Point Cloud with One-stage Learning.- Placental Flattening via Volumetric Parameterization with Dirichlet Energy Regularization.- Fast Polynomial Approximation to Heat Diffusion in Manifolds.- Hierarchical Multi-Geodesic Model for Longitudinal Analysis of Temporal Trajectories of Anatomical Shape and Covariates.- Clustering of longitudinal shape data sets using mixture of separate or branching trajectories.- Group-wise Graph Matching of Cortical Gyral Hinges.- Multi-view Graph Matching of Cortical Landmarks.- Patient-specific Conditional Joint Models of Shape, Image Features and Clinical Indicators.- Surface-Based Spatial Pyramid Matching of Cortical Regions for Analysis of Cognitive Performance.- Prediction.- Diagnosis-guided multi-modal feature selection for prognosis prediction of lung squamous cell carcinoma.- Graph convolution based attention model for personalized disease prediction.- Predicting Early Stages of Neurodegenerative Diseases via Multi-task Low-rank Feature Learning.- Improved Prediction of Cognitive Outcomes via Globally Aligned Imaging Biomarker Enrichments Over Progressions.- Deep Granular Feature-Label Distribution Learning for Neuroimaging-based Infant Age Prediction.- End-to-End Dementia Status Prediction from Brain MRI using Multi-Task Weakly-Supervised Attention Network.- Unified Modeling of Imputation, Forecasting, and Prediction for AD Progression.- LSTM Network for Prediction of Hemorrhagic Transformation in Acute Stroke.- Inter-modality Dependence Induced Data Recovery for MCI Conversion Prediction.- Preprocessing, Prediction and Significance: Framework and Application to Brain Imaging.- Early Prediction of Alzheimer's Disease progression using Variational Autoencoder.- Integrating Heterogeneous Brain Networks for Predicting Brain Disease Conditions.- Detection and Localization.- Uncertainty-informed detection of epileptogenic brain malformations using Bayesian neural networks.- Automated Lesion Detection by Regressing Intensity-Based Distance with a Neural Network.- Intracranial aneurysms detection in 3D cerebrovascular mesh model with ensemble deep learning.- Automated Noninvasive Seizure Detection and Localization Using Switching Markov Models and Convolutional Neural Networks.- Multiple Landmarks Detection using Multi-Agent Reinforcement Learning.- Spatiotemporal Breast Mass Detection Network (MD-Net) in 4D DCE-MRI Images.- Automated Pulmonary Embolism Detection from CTPA Images using an End-to-End Convolutional Neural Network.- Pixel-wise anomaly ratings using Variational Auto-Encoders.- HR-CAM: Precise Localization of pathology using multi-level learning in CNNs.- Novel Iterative Attention Focusing Strategy for Joint Pathology Localization and Diagnosis of MCI Progression.- Automatic Vertebrae Recognition from Arbitrary Spine MRI images by a Hierarchical Self-calibration Detection Framework.- Machine Learning.- Image data validation for medical systems.- Captioning Ultrasound Images Automatically.- Feature Transformers: Privacy Preserving Life Learning Framework for Healthcare Applications.- As easy as 1, 2... 4? Uncertainty in counting tasks for medical imaging.- Generalizable Feature Learning in the Presence of Data Bias and Domain Class Imbalance with Application to Skin Lesion Classification.- Learning task-specific and shared representations in medical imaging.- Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis.- Efficient Ultrasound Image Analysis Models with Sonographer Gaze Assisted Distillation.- Fetal Pose Estimation in Volumetric MRI using 3D Convolution Neural Network.-

Автор: Dinggang Shen; Tianming Liu; Terry M. Peters; Lawr
Название: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
ISBN: 3030322386 ISBN-13(EAN): 9783030322380
Издательство: Springer
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Цена: 98750.00 T
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Optical Imaging.- Enhancing OCT Signal by Fusion of GANs: Improving Statistical Power of Glaucoma Trials.- A Deep Reinforcement Learning Framework for Frame-by-frame Plaque Tracking on Intravascular Optical Coherence Tomography Image.- Multi-Index Optic Disc Quantification via MultiTask Ensemble Learning.- Retinal Abnormalities Recognition Using Regional Multitask Learning.- Unifying Structure Analysis and Surrogate-driven Function Regression for Glaucoma OCT Image Screening.- Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces.- 3D Surface-Based Geometric and Topological Quantification of Retinal Microvasculature in OCT-Angiography via Reeb Analysis.- Limited-Angle Diffuse Optical Tomography Image Reconstruction using Deep Learning.- Data-driven Enhancement of Blurry Retinal Images via Generative Adversarial Networks.- Dual Encoding U-Net for Retinal Vessel Segmentation.- A Deep Learning Design for improving Topology Coherence in Blood Vessel Segmentation.- Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation.- Unsupervised Ensemble Strategy for Retinal Vessel Segmentation.- Fully convolutional boundary regression for retina OCT segmentation.- PM-NET: Pyramid Multi-Label Network for Optic Disc and Cup Segmentation.- Biological Age Estimated from Retinal Imaging: A Novel Biomarker of Aging.- Task Adaptive Metric Space for Medium-Shot Medical Image Classification.- Two-Stream CNN with Loose Pair Training for Multi-modal AMD Categorization.- Deep Multi Label Classification in Affine Subspaces.- Multi-scale Microaneurysms Segmentation Using Embedding Triplet Loss.- A Divide-and-Conquer Approach towards Understanding Deep Networks.- Multiclass segmentation as multitask learning for drusen segmentation in retinal optical coherence tomography.- Active Appearance Model Induced Generative Adversarial Networks for Controlled Data Augmentation.- Biomarker Localization by Combining CNN Classifier and Generative Adversarial Network.- Probabilistic Atlases to Enforce Topological Constraints.- Synapse-Aware Skeleton Generation for Neural Circuits.- Seeing Under the Cover: A Physics Guided Learning Approach for In-Bed Pose Estimation.- EDA-Net: Dense Aggregation of Deep and Shallow Information Achieves Quantitative Photoacoustic Blood Oxygenation Imaging Deep in Human Breast.- Fused Detection of Retinal Biomarkers in OCT Volumes.- Vessel-Net: Retinal Vessel Segmentation under Multi-path Supervision.- Ki-GAN: Knowledge Infusion Generative Adversarial Network for Photoacoustic Image Reconstruction in vivo.- Uncertainty guided semisupervised segmentation of retinal layers in OCT images.- Endoscopy.- Triple ANet: Adaptive Abnormal-aware Attention Network for WCE Image Classification.- Selective Feature Aggregation Network with Area-boundary Constraints for Polyp Segmentation.- Deep Sequential Mosaicking of Fetoscopic Videos.- Landmark-guided Deformable Image Registration for Supervised Autonomous Robotic Tumor Resection.- Multi-View Learning with Feature Level Fusion for Cervical Dysplasia Diagnosis.- Real-time Surface Deformation Recovery from Stereo Videos.- Microscopy.- Rectified Cross-Entropy and Upper Transition Loss for Weakly Supervised Whole Slide Image Classifier.- From Whole Slide Imaging to Microscopy: Deep Microscopy Adaptation Network for Histopathology Cancer Image Classification.- Multi-scale Cell Instance Segmentation with Keypoint Graph based Bounding Boxes.- Improving Nuclei/Gland Instance Segmentation in Histopathology Images by Full Resolution Neural Network and Spatial Constrained Loss.- Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification.- Cell Tracking with Deep Learning for Cell Detection and Motion Estimation in Low-Frame-Rate.- Accelerated ML-assisted Tumor Detection in High-Resolution Histopathology Images.- Pre-operative Overall Survival Time Prediction for Glioblas

Автор: Kensaku Mori; Ichiro Sakuma; Yoshinobu Sato; Chris
Название: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2013
ISBN: 3642407625 ISBN-13(EAN): 9783642407628
Издательство: Springer
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Цена: 46570.00 T
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Описание: The three-volume set LNCS 8149, 8150, and 8151 constitutes the refereed proceedings of the 16th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013, held in Nagoya, Japan, in September 2013.

Автор: Kensaku Mori; Ichiro Sakuma; Yoshinobu Sato; Chris
Название: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2013
ISBN: 3642407595 ISBN-13(EAN): 9783642407598
Издательство: Springer
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Цена: 46570.00 T
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Описание: The three-volume set LNCS 8149, 8150, and 8151 constitutes the refereed proceedings of the 16th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013, held in Nagoya, Japan, in September 2013.

Автор: Maxime Descoteaux; Lena Maier-Hein; Alfred Franz;
Название: Medical Image Computing and Computer-Assisted Intervention ? MICCAI 2017
ISBN: 3319661841 ISBN-13(EAN): 9783319661841
Издательство: Springer
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Цена: 93160.00 T
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Описание: The three-volume set LNCS 10433, 10434, and 10435 constitutes the refereed proceedings of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, held inQuebec City, Canada, in September 2017.
The 255 revised full papers presented were carefully reviewed and selected from 800 submissions in a two-phase review process. The papers have been organized in the following topical sections: Part I: atlas and surface-based techniques; shape and patch-based techniques; registration techniques, functional imaging, connectivity, and brain parcellation; diffusion magnetic resonance imaging (dMRI) and tensor/fiber processing; and image segmentation and modelling. Part II: optical imaging; airway and vessel analysis; motion and cardiac analysis; tumor processing; planning and simulation for medical interventions; interventional imaging and navigation; and medical image computing. Part III: feature extraction and classification techniques; and machine learning in medical image computing.

Автор: Dimitris Metaxas; Leon Axel; Gabor Fichtinger; Gab
Название: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008
ISBN: 3540859896 ISBN-13(EAN): 9783540859895
Издательство: Springer
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Цена: 158380.00 T
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Описание: Constitutes the refereed proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008, held in New York, NY, USA, in September 2008. This two-volume set includes papers related to medical image computing, segmentation, and contributions related to robotics and interventions.

Автор: Maxime Descoteaux; Lena Maier-Hein; Alfred Franz;
Название: Medical Image Computing and Computer Assisted Intervention ? MICCAI 2017
ISBN: 3319661817 ISBN-13(EAN): 9783319661810
Издательство: Springer
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Цена: 102480.00 T
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Описание: The three-volume set LNCS 10433, 10434, and 10435 constitutes the refereed proceedings of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, held inQuebec City, Canada, in September 2017.
The 255 revised full papers presented were carefully reviewed and selected from 800 submissions in a two-phase review process. The papers have been organized in the following topical sections: Part I: atlas and surface-based techniques; shape and patch-based techniques; registration techniques, functional imaging, connectivity, and brain parcellation; diffusion magnetic resonance imaging (dMRI) and tensor/fiber processing; and image segmentation and modelling. Part II: optical imaging; airway and vessel analysis; motion and cardiac analysis; tumor processing; planning and simulation for medical interventions; interventional imaging and navigation; and medical image computing. Part III: feature extraction and classification techniques; and machine learning in medical image computing.