Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

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

AvDinggang Shen,Tianming Liu

Häftad, Engelska, 2019

Del 11766 i serien Lecture Notes in Computer Science

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Beskrivning

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

Utgivningsdatum:2019-10-18
Mått:155 x 235 x 50 mm
Vikt:1 375 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:888
Förlag:Springer Nature Switzerland AG
ISBN:9783030322472

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Systemvetenskap och AI inom Data och IT
Informationsteknik: allmänt inom Data och IT

Innehållsförteckning

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 DilatedMulti-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 Connectivitiesvia 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.