Medical Image Computing and Computer Assisted Intervention - MICCAI 2019 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part III /

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 w...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Shen, Dinggang (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Liu, Tianming (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Peters, Terry M. (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Staib, Lawrence H. (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Essert, Caroline (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Zhou, Sean (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Yap, Pew-Thian (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Khan, Ali (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
Έκδοση:1st ed. 2019.
Σειρά:Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11766
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • 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 Global Optimization for 3D Image Segmentation
  • Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation
  • RSANet: Recurrent Slice-wise Attention Network for Multiple Sclerosis Lesion Segmentation
  • Deep Cascaded Attention Networks for Multi-task Brain Tumor Segmentation
  • Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation
  • 3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation
  • Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion
  • Multi-task Attention-based Semi-supervised Learning for Medical Image Segmentation
  • AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation
  • Automated Parcellation of the Cortex using Structural Connectome Harmonics
  • Hierarchical parcellation of the cerebellum
  • Intrinsic Patch-based Cortical Anatomical Parcellation using Graph Convolutional Neural Network on Surface Manifold
  • Cortical Surface Parcellation using Spherical Convolutional Neural Networks
  • A Soft STAPLE Algorithm Combined with Anatomical Knowledge
  • Diffusion Weighted Magnetic Resonance Imaging
  • Multi-Stage Image Quality Assessment of Diffusion MRI via Semi-Supervised Nonlocal Residual Networks
  • Reconstructing High-Quality Diffusion MRI Data from Orthogonal Slice-Undersampled Data Using Graph Convolutional Neural Networks
  • Surface-based Tracking of U-fibers in the Superficial White Matter
  • Probing Brain Micro-Architecture by Orientation Distribution Invariant Identification of Diffusion Compartments
  • Characterizing Non-Gaussian Diffusion in Heterogeneously Oriented Tissue Microenvironments
  • Topographic Filtering of Tractograms as Vector Field Flows
  • Enabling Multi-Shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE
  • Super-Resolved q-Space Deep Learning
  • Joint Identification of Network Hub Nodes by Multivariate Graph Inference
  • Deep white matter analysis: fast, consistent tractography segmentation across populations and dMRI acquisitions
  • Improved Placental Parameter Estimation Using Data-Driven Bayesian Modelling
  • Optimal experimental design for biophysical modelling in multidimensional diffusion MRI
  • DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography‏
  • Fast and Scalable Optimal Transport for Brain Tractograms
  • A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes
  • Constructing Consistent Longitudinal Brain Networks by Group-wise Graph Learning
  • Functional Neuroimaging (fMRI)
  • Multi-layer temporal network analysis reveals increasing temporal reachability and spreadability in the first two years of life
  • A matched filter decomposition of fMRI into resting and task components
  • Identification of Abnormal Circuit Dynamics in Major Depressive Disorder via Multiscale Neural Modeling of Resting-state fMRI
  • Integrating Functional and Structural Connectivities via Diffusion-Convolution-Bilinear Neural Network
  • Invertible Network for Classification and Biomarker Selection for ASD
  • Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data
  • Revealing Functional Connectivity by Learning Graph Laplacian
  • Constructing Multi-Scale Connectome Atlas by Learning Common Topology of Brain Networks
  • Autism Classification Using Topological Features and Deep Learning: A Cautionary Tale
  • Identify Hierarchical Structures from Task-based fMRI Data via Hybrid Spatiotemporal Neural Architecture Search Net
  • A Deep Learning Framework for Noise Component Detection from Resting-state Functional MRI
  • A Novel Graph Wavelet Model for Brain Multi-Scale Functional-structural Feature Fusion
  • Combining Multiple Behavioral Measures and Multiple Connectomes via Multiway Canonical Correlation Analysis
  • Decoding brain functional connectivity implicated in AD and MCI
  • Interpretable Feature Learning Using Multi-Output Takagi-Sugeno-Kang Fuzzy System for Multi-center ASD Diagnosis
  • Interpretable Multimodality Embedding Of Cerebral Cortex Using Attention Graph Network For Identifying Bipolar Disorder
  • Miscellaneous Neuroimaging
  • Doubly Weak Supervision of Deep Learning Models for Head CT
  • Detecting Acute Strokes from Non-Contrast CT Scan Data Using Deep Convolutional Neural Networks
  • FocusNet:
  • Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images
  • Regression-based Line Detection Network for Delineation of Largely Deformed Brain Midline
  • Siamese U-Net with Healthy Template for Accurate Segmentation of Intracranial Hemorrhage
  • Automated Infarct Segmentation from Follow-up Non-Contrast CT Scans in Patients with Acute Ischemic Stroke Using Dense Multi-Path Contextual Generative Adversarial Network
  • Recurrent sub-volume analysis of head CT scans for the detection of intracranial hemorrhage
  • Cephalometric Landmark Detection by Attentive Feature Pyramid Fusion and Regression-Voting.