Medical Image Computing and Computer Assisted Intervention - MICCAI 2019 : 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part III / edited by Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan.

Format:

Book

Contributor:

Shen, Dinggang, editor.

Liu, Tianming, editor.

Peters, Terry M., 1948 January 5- editor.

Staib, Lawrence H., editor.

Essert, Caroline, editor.

Zhou, Xiangyun Sean, editor.

Yap, Pew-Thian, editor.

Khan, Ali, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 11766.

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11766

Language:

English

Subjects (All):

Optical data processing.

Pattern perception.

Artificial intelligence.

Medical informatics.

Image Processing and Computer Vision.

Pattern Recognition.

Artificial Intelligence.

Health Informatics.

Local Subjects:

Image Processing and Computer Vision.

Pattern Recognition.

Artificial Intelligence.

Health Informatics.

Physical Description:

1 online resource (XXXVIII, 888 pages) : 359 illustrations, 314 illustrations in color.

Edition:

First edition 2019.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-030-32248-9

9783030322489

Access Restriction:

Restricted for use by site license.