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Information Processing in Medical Imaging

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Cover of 'Information Processing in Medical Imaging'

Table of Contents

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    Book Overview
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    Chapter 1 Robust Fréchet Mean and PGA on Riemannian Manifolds with Applications to Neuroimaging
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    Chapter 2 Inconsistency of Template Estimation with the Fréchet Mean in Quotient Space
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    Chapter 3 Kernel Methods for Riemannian Analysis of Robust Descriptors of the Cerebral Cortex
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    Chapter 4 Conditional Local Distance Correlation for Manifold-Valued Data
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    Chapter 5 Stochastic Development Regression on Non-linear Manifolds
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    Chapter 6 Spectral Kernels for Probabilistic Analysis and Clustering of Shapes
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    Chapter 7 Optimal Topological Cycles and Their Application in Cardiac Trabeculae Restoration
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    Chapter 8 From Label Maps to Generative Shape Models: A Variational Bayesian Learning Approach
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    Chapter 9 Constructing Shape Spaces from a Topological Perspective
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    Chapter 10 A Discriminative Event Based Model for Alzheimer’s Disease Progression Modeling
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    Chapter 11 A Vertex Clustering Model for Disease Progression: Application to Cortical Thickness Images
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    Chapter 12 Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
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    Chapter 13 A Novel Dynamic Hyper-graph Inference Framework for Computer Assisted Diagnosis of Neuro-Diseases
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    Chapter 14 A Likelihood-Free Approach for Characterizing Heterogeneous Diseases in Large-Scale Studies
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    Chapter 15 Multi-source Multi-target Dictionary Learning for Prediction of Cognitive Decline
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    Chapter 16 Predicting Interrelated Alzheimer’s Disease Outcomes via New Self-learned Structured Low-Rank Model
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    Chapter 17 Weakly-Supervised Evidence Pinpointing and Description
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    Chapter 18 Quantifying the Uncertainty in Model Parameters Using Gaussian Process-Based Markov Chain Monte Carlo: An Application to Cardiac Electrophysiological Models
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    Chapter 19 Cancer Metastasis Detection via Spatially Structured Deep Network
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    Chapter 20 Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning
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    Chapter 21 Topographic Regularity for Tract Filtering in Brain Connectivity
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    Chapter 22 Riccati-Regularized Precision Matrices for Neuroimaging
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    Chapter 23 Multimodal Brain Subnetwork Extraction Using Provincial Hub Guided Random Walks
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    Chapter 24 Exact Topological Inference for Paired Brain Networks via Persistent Homology
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    Chapter 25 Multivariate Manifold Modelling of Functional Connectivity in Developing Language Networks
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    Chapter 26 Hierarchical Region-Network Sparsity for High-Dimensional Inference in Brain Imaging
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    Chapter 27 A Restaurant Process Mixture Model for Connectivity Based Parcellation of the Cortex
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    Chapter 28 On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task
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    Chapter 29 Discovering Change-Point Patterns in Dynamic Functional Brain Connectivity of a Population
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    Chapter 30 Extracting the Groupwise Core Structural Connectivity Network: Bridging Statistical and Graph-Theoretical Approaches
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    Chapter 31 Estimation of Brain Network Atlases Using Diffusive-Shrinking Graphs: Application to Developing Brains
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    Chapter 32 A Tensor Statistical Model for Quantifying Dynamic Functional Connectivity
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    Chapter 33 Modeling Task fMRI Data via Deep Convolutional Autoencoder
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    Chapter 34 Director Field Analysis to Explore Local White Matter Geometric Structure in Diffusion MRI
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    Chapter 35 Decoupling Axial and Radial Tissue Heterogeneity in Diffusion Compartment Imaging
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    Chapter 36 Bayesian Dictionary Learning and Undersampled Multishell HARDI Reconstruction
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    Chapter 37 Estimation of Tissue Microstructure Using a Deep Network Inspired by a Sparse Reconstruction Framework
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    Chapter 38 HFPRM: Hierarchical Functional Principal Regression Model for Diffusion Tensor Image Bundle Statistics
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    Chapter 39 Orthotropic Thin Shell Elasticity Estimation for Surface Registration
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    Chapter 40 Direct Estimation of Regional Wall Thicknesses via Residual Recurrent Neural Network
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    Chapter 41 Multi-class Image Segmentation in Fluorescence Microscopy Using Polytrees
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    Chapter 42 Direct Estimation of Spinal Cobb Angles by Structured Multi-output Regression
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    Chapter 43 Identifying Associations Between Brain Imaging Phenotypes and Genetic Factors via a Novel Structured SCCA Approach
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    Chapter 44 Frequency Diffeomorphisms for Efficient Image Registration
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    Chapter 45 A Stochastic Large Deformation Model for Computational Anatomy
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    Chapter 46 Symmetric Interleaved Geodesic Shooting in Diffeomorphisms
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    Chapter 47 Unsupervised Domain Adaptation in Brain Lesion Segmentation with Adversarial Networks
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    Chapter 48 Globally Optimal Coupled Surfaces for Semi-automatic Segmentation of Medical Images
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    Chapter 49 Joint Deep Learning of Foreground, Background and Shape for Robust Contextual Segmentation
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    Chapter 50 Automatic Vertebra Labeling in Large-Scale 3D CT Using Deep Image-to-Image Network with Message Passing and Sparsity Regularization
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    Chapter 51 A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction
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    Chapter 52 Population Based Image Imputation
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    Chapter 53 VTrails: Inferring Vessels with Geodesic Connectivity Trees
Attention for Chapter 3: Kernel Methods for Riemannian Analysis of Robust Descriptors of the Cerebral Cortex
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Chapter title
Kernel Methods for Riemannian Analysis of Robust Descriptors of the Cerebral Cortex
Chapter number 3
Book title
Information Processing in Medical Imaging
Published in
Information processing in medical imaging proceedings of the conference, June 2017
DOI 10.1007/978-3-319-59050-9_3
Pubmed ID
Book ISBNs
978-3-31-959049-3, 978-3-31-959050-9
Authors

Suyash P. Awate, Richard M. Leahy, Anand A. Joshi

Abstract

Typical cerebral cortical analyses rely on spatial normalization and are sensitive to misregistration arising from partial homologies between subject brains and local optima in nonlinear registration. In contrast, we use a descriptor of the 3D cortical sheet (jointly modeling folding and thickness) that is robust to misregistration. Our histogram-based descriptor lies on a Riemannian manifold. We propose new regularized nonlinear methods for (i) detecting group differences, using a Mercer kernel with an implicit lifting map to a reproducing kernel Hilbert space, and (ii) regression against clinical variables, using kernel density estimation. For both methods, we employ kernels that exploit the Riemannian structure. Results on simulated and clinical data shows the improved accuracy and stability of our approach in cortical-sheet analysis.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 22%
Researcher 2 22%
Student > Master 2 22%
Student > Postgraduate 1 11%
Unknown 2 22%
Readers by discipline Count As %
Engineering 2 22%
Medicine and Dentistry 2 22%
Computer Science 1 11%
Unknown 4 44%