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Machine Learning in Medical Imaging

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Cover of 'Machine Learning in Medical Imaging'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 From Large to Small Organ Segmentation in CT Using Regional Context
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    Chapter 2 Motion Corruption Detection in Breast DCE-MRI
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    Chapter 3 Detection and Localization of Drosophila Egg Chambers in Microscopy Images
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    Chapter 4 Growing a Random Forest with Fuzzy Spatial Features for Fully Automatic Artery-Specific Coronary Calcium Scoring
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    Chapter 5 Atlas of Classifiers for Brain MRI Segmentation
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    Chapter 6 Dictionary Learning and Sparse Coding-Based Denoising for High-Resolution Task Functional Connectivity MRI Analysis
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    Chapter 7 Yet Another ADNI Machine Learning Paper? Paving the Way Towards Fully-Reproducible Research on Classification of Alzheimer’s Disease
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    Chapter 8 Multi-factorial Age Estimation from Skeletal and Dental MRI Volumes
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    Chapter 9 Automatic Classification of Proximal Femur Fractures Based on Attention Models
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    Chapter 10 Joint Supervoxel Classification Forest for Weakly-Supervised Organ Segmentation
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    Chapter 11 Accurate and Consistent Hippocampus Segmentation Through Convolutional LSTM and View Ensemble
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    Chapter 12 STAR: Spatio-Temporal Architecture for Super-Resolution in Low-Dose CT Perfusion
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    Chapter 13 Classification of Alzheimer’s Disease by Cascaded Convolutional Neural Networks Using PET Images
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    Chapter 14 Finding Dense Supervoxel Correspondence of Cone-Beam Computed Tomography Images
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    Chapter 15 Multi-scale Volumetric ConvNet with Nested Residual Connections for Segmentation of Anterior Cranial Base
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    Chapter 16 Feature Learning and Fusion of Multimodality Neuroimaging and Genetic Data for Multi-status Dementia Diagnosis
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    Chapter 17 3D Convolutional Neural Networks with Graph Refinement for Airway Segmentation Using Incomplete Data Labels
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    Chapter 18 Efficient Groupwise Registration for Brain MRI by Fast Initialization
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    Chapter 19 Sparse Multi-view Task-Centralized Learning for ASD Diagnosis
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    Chapter 20 Inter-subject Similarity Guided Brain Network Modeling for MCI Diagnosis
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    Chapter 21 Scalable and Fault Tolerant Platform for Distributed Learning on Private Medical Data
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    Chapter 22 Triple-Crossing 2.5D Convolutional Neural Network for Detecting Neuronal Arbours in 3D Microscopic Images
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    Chapter 23 Longitudinally-Consistent Parcellation of Infant Population Cortical Surfaces Based on Functional Connectivity
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    Chapter 24 Gradient Boosted Trees for Corrective Learning
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    Chapter 25 Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis
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    Chapter 26 A Point Says a Lot: An Interactive Segmentation Method for MR Prostate via One-Point Labeling
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    Chapter 27 Collage CNN for Renal Cell Carcinoma Detection from CT
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    Chapter 28 Aggregating Deep Convolutional Features for Melanoma Recognition in Dermoscopy Images
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    Chapter 29 Localizing Cardiac Structures in Fetal Heart Ultrasound Video
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    Chapter 30 Deformable Registration Through Learning of Context-Specific Metric Aggregation
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    Chapter 31 Segmentation of Craniomaxillofacial Bony Structures from MRI with a 3D Deep-Learning Based Cascade Framework
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    Chapter 32 3D U-net with Multi-level Deep Supervision: Fully Automatic Segmentation of Proximal Femur in 3D MR Images
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    Chapter 33 Indecisive Trees for Classification and Prediction of Knee Osteoarthritis
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    Chapter 34 Whole Brain Segmentation and Labeling from CT Using Synthetic MR Images
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    Chapter 35 Structural Connectivity Guided Sparse Effective Connectivity for MCI Identification
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    Chapter 36 Fusion of High-Order and Low-Order Effective Connectivity Networks for MCI Classification
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    Chapter 37 Novel Effective Connectivity Network Inference for MCI Identification
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    Chapter 38 Reconstruction of Thin-Slice Medical Images Using Generative Adversarial Network
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    Chapter 39 Neural Network Convolution (NNC) for Converting Ultra-Low-Dose to “Virtual” High-Dose CT Images
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    Chapter 40 Deep-FExt: Deep Feature Extraction for Vessel Segmentation and Centerline Prediction
  42. Altmetric Badge
    Chapter 41 Product Space Decompositions for Continuous Representations of Brain Connectivity
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    Chapter 42 Identifying Autism from Resting-State fMRI Using Long Short-Term Memory Networks
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    Chapter 43 Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging
  45. Altmetric Badge
    Chapter 44 Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks
Attention for Chapter 43: Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging
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Chapter title
Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging
Chapter number 43
Book title
Machine Learning in Medical Imaging
Published in
Machine learning in medical imaging. MLMI (Workshop), September 2017
DOI 10.1007/978-3-319-67389-9_43
Pubmed ID
30035274
Book ISBNs
978-3-31-967388-2, 978-3-31-967389-9
Authors

Dmitry Petrov, Boris A. Gutman, Shih-Hua (Julie) Yu, Kathryn Alpert, Artemis Zavaliangos-Petropulu, Dmitry Isaev, Jessica A. Turner, Theo G. M. van Erp, Lei Wang, Lianne Schmaal, Dick Veltman, Paul M. Thompson, Petrov, Dmitry, Gutman, Boris A., Yu, Shih-Hua (Julie), Alpert, Kathryn, Zavaliangos-Petropulu, Artemis, Isaev, Dmitry, Turner, Jessica A., Erp, Theo G. M., Wang, Lei, Schmaal, Lianne, Veltman, Dick, Thompson, Paul M.

Abstract

As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.

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 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 45 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 24%
Other 5 11%
Student > Ph. D. Student 5 11%
Student > Master 4 9%
Student > Doctoral Student 3 7%
Other 8 18%
Unknown 9 20%
Readers by discipline Count As %
Psychology 8 18%
Medicine and Dentistry 5 11%
Neuroscience 4 9%
Biochemistry, Genetics and Molecular Biology 3 7%
Computer Science 2 4%
Other 6 13%
Unknown 17 38%

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