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Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

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Table of Contents

  1. Altmetric Badge
    Book Overview
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    Chapter 1 Feature Selection Based on Iterative Canonical Correlation Analysis for Automatic Diagnosis of Parkinson’s Disease
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    Chapter 2 Identifying Relationships in Functional and Structural Connectome Data Using a Hypergraph Learning Method
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    Chapter 3 Ensemble Hierarchical High-Order Functional Connectivity Networks for MCI Classification
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    Chapter 4 Outcome Prediction for Patient with High-Grade Gliomas from Brain Functional and Structural Networks
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    Chapter 5 Mammographic Mass Segmentation with Online Learned Shape and Appearance Priors
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    Chapter 6 Differential Dementia Diagnosis on Incomplete Data with Latent Trees
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    Chapter 7 Bridging Computational Features Toward Multiple Semantic Features with Multi-task Regression: A Study of CT Pulmonary Nodules
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    Chapter 8 Robust Cancer Treatment Outcome Prediction Dealing with Small-Sized and Imbalanced Data from FDG-PET Images
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    Chapter 9 Structured Sparse Kernel Learning for Imaging Genetics Based Alzheimer’s Disease Diagnosis
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    Chapter 10 Semi-supervised Hierarchical Multimodal Feature and Sample Selection for Alzheimer’s Disease Diagnosis
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    Chapter 11 Stability-Weighted Matrix Completion of Incomplete Multi-modal Data for Disease Diagnosis
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    Chapter 12 Employing Visual Analytics to Aid the Design of White Matter Hyperintensity Classifiers
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    Chapter 13 The Automated Learning of Deep Features for Breast Mass Classification from Mammograms
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    Chapter 14 Multimodal Deep Learning for Cervical Dysplasia Diagnosis
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    Chapter 15 Learning from Experts: Developing Transferable Deep Features for Patient-Level Lung Cancer Prediction
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    Chapter 16 DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field
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    Chapter 17 Deep Retinal Image Understanding
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    Chapter 18 3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes
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    Chapter 19 Deep Neural Networks for Fast Segmentation of 3D Medical Images
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    Chapter 20 SpineNet: Automatically Pinpointing Classification Evidence in Spinal MRIs
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    Chapter 21 A Deep Learning Approach for Semantic Segmentation in Histology Tissue Images
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    Chapter 22 Spatial Clockwork Recurrent Neural Network for Muscle Perimysium Segmentation
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    Chapter 23 Automated Age Estimation from Hand MRI Volumes Using Deep Learning
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    Chapter 24 Real-Time Standard Scan Plane Detection and Localisation in Fetal Ultrasound Using Fully Convolutional Neural Networks
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    Chapter 25 3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients
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    Chapter 26 From Local to Global Random Regression Forests: Exploring Anatomical Landmark Localization
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    Chapter 27 Regressing Heatmaps for Multiple Landmark Localization Using CNNs
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    Chapter 28 Self-Transfer Learning for Weakly Supervised Lesion Localization
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    Chapter 29 Automatic Cystocele Severity Grading in Ultrasound by Spatio-Temporal Regression
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    Chapter 30 Graphical Modeling of Ultrasound Propagation in Tissue for Automatic Bone Segmentation
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    Chapter 31 Bayesian Image Quality Transfer
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    Chapter 32 Wavelet Appearance Pyramids for Landmark Detection and Pathology Classification: Application to Lumbar Spinal Stenosis
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    Chapter 33 A Learning-Free Approach to Whole Spine Vertebra Localization in MRI
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    Chapter 34 Automatic Quality Control for Population Imaging: A Generic Unsupervised Approach
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    Chapter 35 A Cross-Modality Neural Network Transform for Semi-automatic Medical Image Annotation
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    Chapter 36 Sub-category Classifiers for Multiple-instance Learning and Its Application to Retinal Nerve Fiber Layer Visibility Classification
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    Chapter 37 Vision-Based Classification of Developmental Disorders Using Eye-Movements
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    Chapter 38 Scalable Unsupervised Domain Adaptation for Electron Microscopy
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    Chapter 39 Automated Diagnosis of Neural Foraminal Stenosis Using Synchronized Superpixels Representation
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    Chapter 40 Automated Segmentation of Knee MRI Using Hierarchical Classifiers and Just Enough Interaction Based Learning: Data from Osteoarthritis Initiative
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    Chapter 41 Dynamically Balanced Online Random Forests for Interactive Scribble-Based Segmentation
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    Chapter 42 Orientation-Sensitive Overlap Measures for the Validation of Medical Image Segmentations
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    Chapter 43 High-Throughput Glomeruli Analysis of $$\mu $$ CT Kidney Images Using Tree Priors and Scalable Sparse Computation
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    Chapter 44 A Surface Patch-Based Segmentation Method for Hippocampal Subfields
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    Chapter 45 Automatic Lymph Node Cluster Segmentation Using Holistically-Nested Neural Networks and Structured Optimization in CT Images
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    Chapter 46 Evaluation-Oriented Training via Surrogate Metrics for Multiple Sclerosis Segmentation
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    Chapter 47 Corpus Callosum Segmentation in Brain MRIs via Robust Target-Localization and Joint Supervised Feature Extraction and Prediction
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    Chapter 48 Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
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    Chapter 49 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
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    Chapter 50 Model-Based Segmentation of Vertebral Bodies from MR Images with 3D CNNs
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    Chapter 51 Pancreas Segmentation in MRI Using Graph-Based Decision Fusion on Convolutional Neural Networks
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    Chapter 52 Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation
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    Chapter 53 Topology Aware Fully Convolutional Networks for Histology Gland Segmentation
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    Chapter 54 HeMIS: Hetero-Modal Image Segmentation
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    Chapter 55 Deep Learning for Multi-task Medical Image Segmentation in Multiple Modalities
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    Chapter 56 Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images
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    Chapter 57 Gland Instance Segmentation by Deep Multichannel Side Supervision
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    Chapter 58 Enhanced Probabilistic Label Fusion by Estimating Label Confidences Through Discriminative Learning
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    Chapter 59 Feature Sensitive Label Fusion with Random Walker for Atlas-Based Image Segmentation
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    Chapter 60 Deep Fusion Net for Multi-atlas Segmentation: Application to Cardiac MR Images
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    Chapter 61 Prior-Based Coregistration and Cosegmentation
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    Chapter 62 Globally Optimal Label Fusion with Shape Priors
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    Chapter 63 Joint Segmentation and CT Synthesis for MRI-only Radiotherapy Treatment Planning
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    Chapter 64 Regression Forest-Based Atlas Localization and Direction Specific Atlas Generation for Pancreas Segmentation
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    Chapter 65 Accounting for the Confound of Meninges in Segmenting Entorhinal and Perirhinal Cortices in T1-Weighted MRI
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    Chapter 66 7T-Guided Learning Framework for Improving the Segmentation of 3T MR Images
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    Chapter 67 Multivariate Mixture Model for Cardiac Segmentation from Multi-Sequence MRI
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    Chapter 68 Fast Fully Automatic Segmentation of the Human Placenta from Motion Corrupted MRI
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    Chapter 69 Multi-organ Segmentation Using Vantage Point Forests and Binary Context Features
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    Chapter 70 Multiple Object Segmentation and Tracking by Bayes Risk Minimization
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    Chapter 71 Crowd-Algorithm Collaboration for Large-Scale Endoscopic Image Annotation with Confidence
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    Chapter 72 Emphysema Quantification on Cardiac CT Scans Using Hidden Markov Measure Field Model: The MESA Lung Study
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    Chapter 73 Cutting Out the Middleman: Measuring Nuclear Area in Histopathology Slides Without Segmentation
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    Chapter 74 Subtype Cell Detection with an Accelerated Deep Convolution Neural Network
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    Chapter 75 Imaging Biomarker Discovery for Lung Cancer Survival Prediction
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    Chapter 76 3D Segmentation of Glial Cells Using Fully Convolutional Networks and k-Terminal Cut
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    Chapter 77 Detection of Differentiated vs. Undifferentiated Colonies of iPS Cells Using Random Forests Modeled with the Multivariate Polya Distribution
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    Chapter 78 Detecting 10,000 Cells in One Second
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    Chapter 79 A Hierarchical Convolutional Neural Network for Mitosis Detection in Phase-Contrast Microscopy Images
  81. Altmetric Badge
    Chapter 80 Erratum to: A Learning-Free Approach to Whole Spine Vertebra Localization in MRI
Attention for Chapter 4: Outcome Prediction for Patient with High-Grade Gliomas from Brain Functional and Structural Networks
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Chapter title
Outcome Prediction for Patient with High-Grade Gliomas from Brain Functional and Structural Networks
Chapter number 4
Book title
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016
Published in
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, January 2016
DOI 10.1007/978-3-319-46723-8_4
Pubmed ID
Book ISBNs
978-3-31-946722-1, 978-3-31-946723-8
Authors

Luyan Liu, Han Zhang, Islem Rekik, Xiaobo Chen, Qian Wang, Dinggang Shen

Abstract

High-grade glioma (HGG) is a lethal cancer, which is characterized by very poor prognosis. To help optimize treatment strategy, accurate preoperative prediction of HGG patient's outcome (i.e., survival time) is of great clinical value. However, there are huge individual variability of HGG, which produces a large variation in survival time, thus making prognostic prediction more challenging. Previous brain imaging-based outcome prediction studies relied only on the imaging intensity inside or slightly around the tumor, while ignoring any information that is located far away from the lesion (i.e., the "normal appearing" brain tissue). Notably, in addition to altering MR image intensity, we hypothesize that the HGG growth and its mass effect also change both structural (can be modeled by diffusion tensor imaging (DTI)) and functional brain connectivities (estimated by functional magnetic resonance imaging (rs-fMRI)). Therefore, integrating connectomics information in outcome prediction could improve prediction accuracy. To this end, we unprecedentedly devise a machine learning-based HGG prediction framework that can effectively extract valuable features from complex human brain connectome using network analysis tools, followed by a novel multi-stage feature selection strategy to single out good features while reducing feature redundancy. Ultimately, we use support vector machine (SVM) to classify HGG outcome as either bad (survival time ≤ 650 days) or good (survival time >650 days). Our method achieved 75 % prediction accuracy. We also found that functional and structural networks provide complementary information for the outcome prediction, thus leading to increased prediction accuracy compared with the baseline method, which only uses the basic clinical information (63.2 %).

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Mendeley readers

The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 44 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 25%
Student > Ph. D. Student 7 16%
Student > Postgraduate 4 9%
Student > Bachelor 4 9%
Student > Doctoral Student 2 5%
Other 5 11%
Unknown 11 25%
Readers by discipline Count As %
Medicine and Dentistry 11 25%
Computer Science 9 20%
Neuroscience 6 14%
Engineering 4 9%
Psychology 1 2%
Other 2 5%
Unknown 11 25%