Information Processing in Medical Imaging : 26th International Conference, IPMI 2019, Hong Kong, China, June 2-7, 2019, Proceedings / edited by Albert C. S. Chung, James C. Gee, Paul A. Yushkevich, Siqi Bao.

Format:

Book

Contributor:

Chung, Albert C. S., Editor.

Gee, James C., Editor.

Yushkevich, Paul A., Editor.

Bao, Siqi, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 11492

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11492

Language:

English

Subjects (All):

Computer vision.

Artificial intelligence.

Computer science-Mathematics.

Medical informatics.

Computer science.

Operating systems (Computers).

Computer Vision.

Artificial Intelligence.

Mathematics of Computing.

Health Informatics.

Models of Computation.

Operating Systems.

Local Subjects:

Computer Vision.

Artificial Intelligence.

Mathematics of Computing.

Health Informatics.

Models of Computation.

Operating Systems.

Physical Description:

1 online resource (XIX, 884 pages) : 517 illustrations, 331 illustrations in color.

Edition:

1st ed. 2019.

Contained In:

Springer Nature eBook

Place of Publication:

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

System Details:

text file PDF

Summary:

This book constitutes the proceedings of the 26th International Conference on Information Processing in Medical Imaging, IPMI 2019, held at the Hong Kong University of Science and Technology, Hong Kong, China, in June 2019. The 69 full papers presented in this volume were carefully reviewed and selected from 229 submissions. They were organized in topical sections on deep learning and segmentation; classification and inference; reconstruction; disease modeling; shape, registration; learning motion; functional imaging; and white matter imaging. The book also includes a number of post papers. .

Contents:

Segmentation

A Bayesian Neural Net to Segment Images with Uncertainty Estimates and Good Calibration

Explicit Topological Priors for Deep-Learning Based Image Segmentation Using Persistent Homology

Semi-Supervised and Task-Driven Data Augmentation

Classification and Inference

Analyzing Brain Morphology on the Bag-of-Features Manifold

Modeling and Inference of Spatio-Temporal Protein Dynamics Across Brain Networks

Deep Learning

InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction

Adaptive Graph Convolution Pooling for Brain Surface Analysis

On Training Deep 3D CNN Models with Dependent Samples in Neuroimaging

A Deep Neural Network for Manifold-Valued Data with Applications to Neuroimaging

Improved Disease Classification in Chest X-rays with Transferred Features from Report Generation

Reconstruction

Limited Angle Tomography Reconstruction: Synthetic Reconstruction via Unsupervised Sinogram Adaptation

Improving Generalization of Deep Networks for Inverse Reconstruction of Image Sequences

Disease Modeling

Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia

Shape

Minimizing Non-Holonomicity: Finding Sheets in Fibrous Structures

Learning Low-Dimensional Representations of Shape Data Sets with Diffeomorphic Autoencoders

Diffeomorphic Medial Modeling

Controlling Meshes via Curvature: Spin Transformations for Pose-Invariant Shape Processing

Registration

Local Optimal Transport for Functional Brain Template Estimation

Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations

Learning Motion

Real-Time 2D-3D Deformable Registration with Deep Learning and Application to Lung Radiotherapy Targeting

Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces

Functional Imaging

Integrating Convolutional Neural Networks and Probabilistic Graphical Modeling for Epileptic Seizure Detection in Multichannel EEG

A Novel Sparse Overlapping Modularized Gaussian Graphical Model for Functional Connectivity Estimation

White Matter Imaging

Asymmetry Spectrum Imaging for Baby Diffusion Tractography

A Fast Fiber k-Nearest-Neighbor Algorithm with Application to Group-Wise White Matter Topography Analysis

Posters

3D Organ Shape Reconstruction from Topogram Images

A Cross-Center Smoothness Prior for Variational Bayesian Brain Tissue Segmentation

A Graph Model of the Lungs with MorphologyBased Structure for Tuberculosis Type Classification

A Longitudinal Model for Tau Aggregation in Alzheimers Disease Based on Structural Connectivity

Accurate Nuclear Segmentation with Center Vector Encoding

Bayesian Longitudinal Modeling of Early Stage Parkinsons Disease Using DaTscan Images

Brain Tumor Segmentation on MRI with Missing Modalities

Contextual Fibre Growth to Generate Realistic Axonal Packing for Diffusion MRI Simulation

DeepCenterline: a Multi-task Fully Convolutional Network for Centerline Extraction

ECKO: Ensemble of Clustered Knockoffs for Robust Multivariate Inference on fMRI Data

FastReg: Fast Non-Rigid Registration via Accelerated Optimisation on the Manifold of Diffeomorphisms

Graph Convolutional Nets for Tool Presence Detection in Surgical Videos

High-Order Oriented Cylindrical Flux for Curvilinear Structure Detection and Vessel Segmentation

Joint CS-MRI Reconstruction and Segmentation with a Unified Deep Network

Learning a Conditional Generative Model for Anatomical Shape Analysis

Manifold Exploring Data Augmentation with Geometric Transformations for Increased Performance and Robustness

Multifold Acceleration of Diffusion MRI via Deep Learning Reconstruction from Slice-Undersampled Data

Riemannian Geometry Learning for Disease Progression Modelling

Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model

Shrinkage Estimation on the Manifold of Symmetric Positive-Definite Matrices with Applications to Neuroimaging

Simultaneous Spatial-temporal Decomposition of Connectome-Scale Brain Networks by Deep Sparse Recurrent Auto-Encoders

Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention

A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces

A Geometric Framework for Feature Mappings in Multimodal Fusion of Brain Image Data

A Hierarchical Manifold Learning Framework for High-Dimensional Neuroimaging Data

A Model for Elastic Evolution on Foliated Shapes

Analyzing Mild Cognitive Impairment Progression via Multi-view Structural Learning

New Graph-Blind Convolutional Network for Brain Connectome Data Analysis

CIA-Net: Robust Nuclei Instance Segmentation with Contour-Aware Information Aggregation

Data-Driven Model Order Reduction For Diffeomorphic Image Registration

DGR-Net: Deep Groupwise Registration of Multispectral Images

Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery

Generalizations of Ripleys K-Function with Application to Space Curves

Group Level MEG/EEG Source Imaging via Optimal Transport: Minimum Wasserstein Estimates

InSpect: INtegrated SPECTral Component Estimation and Mapping for Multi-Contrast Microstructural MRI

Joint Inference on Structural and Diffusion MRI for Sequence-Adaptive Bayesian Segmentation of Thalamic Nuclei with Probabilistic Atlases

Learning-Based Optimization of the Under-Sampling Pattern in MRI

Melanoma Recognition via Visual Attention

Nonlinear Markov Random Fields Learned via Backpropagation

Robust Biophysical Parameter Estimation with a Neural Network Enhanced Hamiltonian Markov Chain Monte Carlo Sampler

SHAMANN: Shared Memory Augmented Neural Networks

Signet Ring Cell Detection With a Semi-supervised Learning Framework

Spherical U-Net on Cortical Surfaces: Methods and Applications

Variational Autoencoder with Truncated Mixture of Gaussians for Functional Connectivity Analysis.

Other Format:

Printed edition:

ISBN:

978-3-030-20351-1

9783030203511

Access Restriction:

Restricted for use by site license.