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Medical Image Computing and Computer Assisted Intervention – MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VII de Anne L. Martel

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VII: Lecture Notes in Computer Science, cartea 12267

Editat de Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
en Limba Engleză Paperback – 3 oct 2020
The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic.
The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections:
Part I: machine learning methodologies
Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks
Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis
Part IV: segmentation; shape models and landmark detection
Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology
Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging
Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography
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Specificații

ISBN-13: 9783030597276
ISBN-10: 303059727X
Pagini: 817
Ilustrații: XXXVII, 817 p. 30 illus.
Dimensiuni: 155 x 235 mm
Greutate: 1.18 kg
Ediția:1st ed. 2020
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Image Processing, Computer Vision, Pattern Recognition, and Graphics

Locul publicării:Cham, Switzerland

Cuprins

Brain Development and Atlases.- A New Metric for Characterizing Dynamic Redundancy of Dense Brain Chronnectome and Its Application to Early Detection of Alzheimer's Disease.- A computational framework for dissociating development-related from individually variable flexibility in regional modularity assignment in early infancy.- Domain-invariant Prior Knowledge Guided Attention Networks for Robust Skull Stripping of Developing Macaque Brains.- Parkinson's Disease Detection from fMRI-derived Brainstem Regional Functional Connectivity Networks.- Persistent Feature Analysis of Multimodal Brain Networks Using Generalized Fused Lasso for EMCI Identification.- Recovering Brain Structural Connectivity from Functional Connectivity via Multi-GCN based Generative Adversarial Network.- From Connectomic to Task-evoked Fingerprints: Individualized Prediction of Task Contrasts from Resting-state Functional Connectivity.- Disentangled Intensive Triplet Autoencoder for Infant Functional Connectome Fingerprinting.- COVLET: Covariance-based Wavelet-like Transform for Statistical Analysis of Brain Characteristics in Children.- Species-Shared and -Specific Structural Connections Revealed by Dirty Multi-Task Regression.- Self-weighted Multi-Task Learning for Subjective Cognitive Decline Diagnosis.- Unified Brain Network with Functional and Structural Data.- Integrating Similarity Awareness and Adaptive Calibration in Graph Convolution Network to Predict Disease.- Infant Cognitive Scores Prediction With Multi-stream Attention-based Temporal Path Signature Features.- Masked Multi-Task Network for Case-level Intracranial Hemorrhage Classification in Brain CT Volumes.- Deep Graph Normalizer: A Geometric Deep Learning Approach for Estimating Connectional Brain Templates.- Supervised Multi-topology Network Cross-diffusion for Population-Driven Brain Network Atlas Estimation.- Partial Volume Segmentation of Brain MRI Scans of any Resolution and Contrast.- BDB-Net: Boundary-enhanced DualBranch Network for Whole Brain Segmentation.- Brain Age Estimation From MRI Using a Two-Stage Cascade Network with a Ranking Loss.- Context-Aware Refinement Network Incorporating Structural Connectivity Prior for Brain Midline Delineation.- Optimizing Visual Cortex Parameterization with Error-Tolerant Teichmüller Map in Retinotopic Mapping.- Multi-Scale Enhanced Graph Convolutional Network for Early Mild Cognitive Impairment Detection.- Construction of Spatiotemporal Infant Cortical Surface Functional Templates.- DWI and Tractography.- Tract Dictionary Learning for Fast and Robust Recognition of Fiber Bundles.- Globally Optimized Super-Resolution of Diffusion MRI Data via Fiber Continuity.- White Matter Tract Segmentation with Self-supervised Learning.- Estimating Tissue Microstructure with Undersampled Diffusion Data via Graph Convolutional Neural Networks.- Tractogram filtering of anatomically non-plausible fibers with geometric deep learning.- Unsupervised Deep Learning for Susceptibility Distortion Correction in Connectome Imaging.- Hierarchical geodesic modeling on the diffusion orientation distribution function for longitudinal DW-MRI analysis.- TRAKO: Efficient Transmission of Tractography Data for Visualization.- Spatial Semantic-Preserving Latent Space Learning for Accelerated DWI Diagnostic Report Generation.- Trajectories from Distribution-valued Functional Curves: A Unified Wasserstein Framework.- Characterizing Intra-Soma Diffusion with Spherical Mean Spectrum Imaging.- Functional Brain Networks.- Estimating Common Harmonic Waves of Brain Networks on Stiefel Manifold.- Neural Architecture Search for Optimization of Spatial-temporal Brain Network Decomposition.- Attention-Guided Deep Graph Neural Network for Longitudinal Alzheimer’s Disease Analysis.- Enriched Representation Learning in Resting-State fMRI for Early MCI Diagnosis.- Whole MILC: generalizing learned dynamics across tasks, datasets, and populations.- A physics-informed geometric learning model for pathological tau spread in Alzheimer's disease.- A deep pattern recognition approach for inferring respiratory volume fluctuations from fMRI data.- A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism.- Poincare embedding reveals edge-based functional networks of the brain.- The constrained network-based statistic: a new level of inference for neuroimaging.- Learning Personal Representations from fMRIby Predicting Neurofeedback Performance.- A 3D Convolutional Encapsulated Long Short-Term Memory (3DConv-LSTM) Model for Denoising fMRI Data.- Detecting Changes of Functional Connectivity by Dynamic Graph Embedding Learning.- Discovering Functional Brain Networks with 3D Residual Autoencoder (ResAE).- Spatiotemporal Attention Autoencoder (STAAE) for ADHD Classification.- Global Diffeomorphic Phase Alignment of Time-series from Resting-state fMRI Data.- Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis.- A shared neural encoding model for the prediction of subject-specific fMRI response.- Neuroimaging.- Topology-Aware Generative Adversarial Network for Joint Prediction of Multiple Brain Graphs from a Single Brain Graph.- Edge-variational Graph Convolutional Networks for Uncertainty-aware Disease Prediction.- Fisher-Rao Regularized Transport Analysis of the Glymphatic System and Waste Drainage.- Joint Neuroimage Synthesis and Representation Learning for Conversion Prediction of Subjective Cognitive Decline.- Differentiable Deconvolution for Improved Stroke Perfusion Analysis.- Spatial Similarity-Aware Learning and Fused Deep Polynomial Network for Detection of Obsessive-Compulsive Disorder.- Deep Representation Learning For Multimodal Brain Networks.- Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis.- Patch-based abnormality maps for improved deep learning-based classification of Huntington's disease.- A Deep Spatial Context Guided Framework for Infant Brain Subcortical Segmentation.- Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE.- Spatial Component Analysis to Mitigate Multiple Testing in Voxel-Based Analysis.- MAGIC: Multi-scale Heterogeneity Analysis and Clustering for Brain Diseases.- PIANO: Perfusion Imaging via Advection-diffusion.- Hierarchical Bayesian Regression for Multi-Site Normative Modeling of Neuroimaging Data.- Image-level Harmonization of Multi-Site Data using Image-and-Spatial Transformer Networks.- A Disentangled Latent Space for Cross-Site MRI Harmonization.- Automated Acquisition Planning for Magnetic Resonance Spectroscopy in Brain Cancer.- Positron Emission Tomography.- Simultaneous Denoising and Motion Estimation for Low-dose Gated PET using a Siamese Adversarial Network with Gate-to-Gate Consistency Learning.- Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy.- Multi-Modality Information Fusionfor Radiomics-based Neural Architecture Search.- Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network.- Rethinking PET Image Reconstruction: Ultra-Low-Dose, Sinogram and Deep Learning.- Clinically Translatable Direct Patlak Reconstruction from Dynamic PET with Motion Correction Using Convolutional Neural Network.- Collimatorless Scintigraphy for Imaging Extremely Low Activity Targeted Alpha Therapy (TAT) with Weighted Robust Least Square (WRLS).