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Auto-Segmentation for Radiation Oncology: State of the Art
Introduction
Evolution of Auto-Segmentation
Evaluation of Auto-Segmentation
Benchmark Dataset
Clinical Implementation Concerns
References
I Multi-Atlas for Auto-Segmentation
Introduction to Multi-Atlas Auto-Segmentation
Introduction
Database Construction
Atlas Selection
Query Image Registration
Label Fusion
Label Post-Processing
Summary of This Part of the Book
References
Evaluation of Atlas Selection: How Close Are We to Optimal Selection?
Motivation for Atlas Selection
Methods of Atlas Selection
Evaluation of Image-Based Atlas Selection
Implementation
Brute-Force Search
Atlas Selection Performance Assessment
Discussion and Implications for Atlas Selection
Limitations
Impact of Atlas Selection on Clinical Practice
Summary and Recommendations for Future Research
References
Deformable Registration Choices for Multi-Atlas Segmentation
Introduction
Deformable Registration
B-Spline Registration
Demons Algorithm
Plastimatch MABS Implementation Details
Evaluation Metrics
Experimental steps
Results
Summary
References
Evaluation of a Multi-Atlas Segmentation System
Introduction
Methods
Patient Data
Online Atlas Selection for Multi-Atlas Segmentation
First Phase of Atlas Selection
Deformable Image Registration
Second Phase of Atlas Selection
Contour Fusion
Evaluation Metrics
Esophagus Segmentation for Head and Neck Cancer Patients
Validation Using Public Benchmark Dataset
Results
Atlas Ranking and Selection
Esophagus Segmentation for Head and Neck Cancer Patients
Validation with Public Benchmark Dataset
Discussion
Conclusions
References
II Deep Learning for Auto-Segmentation
Introduction to Deep Learning-Based Auto-Contouring for Radiotherapy
Introduction
Historical Context
Artificial Neural Networks
Convolution Neural Networks
Computational Power
What Makes Deep Learning-Based Contouring So Different to Atlas-Based or Model-Based Approaches
Underlying Assumptions
Use of Data
Degrees of Freedom
Summary of This Part of the Book
References
Deep Learning Architecture Design for Multi-Organ Segmentation
Introduction
Deep Learning Architecture in Medical Image Multi-Organ Segmentation
Auto-Encoder Methods
Auto-Encoder and Its Variants
Overview of Works
Discussion
CNN Methods
Network Designs
Overview of Works
Discussion
FCN Methods
Network Designs
Overview of Works
Discussion
GAN Methods
Network Designs
Overview of Works
Discussion
R-CNN Methods
Network Designs
Overview of Works
Discussion
Hybrid Methods
Network Designs
Overview of Works
Discussion
Benchmark
Conclusion
Acknowledgments
References
Comparison of 2D and 3D U-Nets for Organ Segmentation
Introduction
Structures of 2D and 3D U-Nets
D U-Net
D U-Net
Experimental Results
Datasets
Evaluation Metrics
Implementation Details
Results
Discussions
Summary
References
Organ-Specific Segmentation Versus Multi-Class Segmentation Using U-Net
Introduction
Materials and Methods
Datasets
Network Structure
Pre-Processing and Downsampling
Quantitative Evaluation Metrics
Implementation and Comparison Experiments
Results
Discussion
Conclusions
Acknowledgments
References
Effect of Loss Functions in Deep Learning-Based Segmentation
Introduction
Admissibility of a Loss Function
Presenting the Problem
Common Loss Functions
Mean Squared Error
Cross Entropy
Binary Cross Entropy
Categorical Cross Entropy
Dice Loss
Hausdorff Distance Loss
Dealing with Class Imbalance
Weighted Cross Entropy
Generalized Dice Loss
No-Background Dice Loss
Focal Loss
Sensitivity Specificity Loss
Tversky Loss
Compound Loss Functions
Dice + Cross Entropy
Dice + Focal Loss
Non-Linear Combinations
Dealing with Imperfect Data
Evaluating a Loss Function
References
Data Augmentation for Training Deep Neural Networks
Overview
Introduction and Literature Review
Geometric Transformations
Intensity Transformation
Artificial Data Generation
Applications of Data Augmentation
Datasets and Image Preprocessing
Training, Validation, and Testing for Organ Segmentation
Evaluation Criteria
Results
Discussion
Summary
Acknowledgments
References
Identifying Possible Scenarios Where a Deep Learning Auto-Segmentation Model Could Fail
Background
Site-Specific Models
Limitations of Training Data
Deep Learning Architecture
Two-Stage U-Net Model
Image Preprocessing
Stage 1: Localization through Coarse Segmentations
Stage 2: OAR Segmentation through Fine-Detail Segmentation
Test-Time Cluster Cropping Technique
Quantitative and Qualitative Review of Auto-Segmentations
Performance on Challenge Test Set
Different Anatomical Sites
Head and Neck Scans
Abdominal Scans
Breast Cancer Simulation Scans
Different Clinical Presentations
Atelectasis and Pleural Effusion
Presence of Motion Management Devices
Use of Contrast and the Presence of Implanted Devices
Adapting to the Unseen
Discussion and Conclusions
Acknowledgments
References
III Clinical Implementation Concerns
Clinical Commissioning Guidelines
Introduction
Stages in Clinical Commissioning
Need for Robust and Clinically Useful Metrics
Need for Curated Datasets for Clinical Commissioning
Auto-Segmentation Clinical Validation Studies – Current State-of-the-Art
Data Curation Guidelines for Radiation Oncology
Evaluation Metrics Guidelines for Clinical Commissioning
Commissioning and Safe Use in the Clinic
Training and Validation Phase
Testing and Verification Phase
Summary
References
Data Curation Challenges for Artificial Intelligence
Introduction
The Complexity of Medical Imaging
The Challenge of Generalizability and Data Heterogeneity
Data Selection
The Need for Large Quantities of Data
Barriers to Sharing Patient Data and Distributed Deep Learning
Data Quality Issues
Data Annotations
Data Curation via Competitions
Bias and Curation of Fair Data
Overview of Data Curation Process
Conclusion
References
On the Evaluation of Auto-Contouring in Radiotherapy
Introduction
Quantitative Evaluation
Strengths and Limitations
Implementation
Classification Accuracy
Implementation
Advantages and Limitations
Dice Similarity Coefficient
Implementation
Advantages and Limitations
Distance Measures
Hausdorff Distance
% Hausdorff Distance
Average Distance
Implementation
Advantages and Limitations
Geometric Properties
Centroid Location Comparison
Volume Comparison
Implementation
Advantages and Limitations
Measures of Estimated Editing
Implementation
Advantages and Limitations
Handling Inter-Observer Variation in Quantitative Assessment
Summary of Quantitative Evaluation
Example Implementation
Subjective Evaluation
The Acceptance of Contours
The Source of Contouring
The Preference for Contouring
Challenges of Subjective Assessment
Summary of Subjective Evaluation
Assessment Based on Intended Clinical Use
Evaluation of Time Saving
Challenges for Study Design
Impact of Auto-Contouring on Planning
Challenges for Study Design
Summary of Clinical Impact Evaluation
Discussion and Recommendations
References
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