Deep Network Design for Medical Image Computing: Principles and Applications

Front Cover
Deep Network Design for Medical Image Computing
Copyright
Contents
List of figures
Acknowledgments
1 Introduction
	1.1 Medical image computing
		1.1.1 Medical image reconstruction
		1.1.2 Medical image analysis
		1.1.3 Medical image computing as functional approximation
	1.2 Deep learning design principles
		1.2.1 Computer vision techniques for medical image computing
		1.2.2 Machine learning techniques for medical image computing
		1.2.3 Medical domain knowledge
	1.3 Chapter organization
	References
2 Deep learning basics
	2.1 Convolutional neural networks
		2.1.1 3D convolutional neural networks
	2.2 Recurrent neural networks
		2.2.1 Long short-term memory
		2.2.2 Bidirectional RNN
	2.3 Deep image-to-image networks
		2.3.1 Retaining spatial resolutions
		2.3.2 Fully convolutional networks
		2.3.3 Encoder–decoder networks
	2.4 Deep generative networks
		2.4.1 Basic models
	References
Part 1 Deep network design for medical image analysis and selected applications
	3 Classification: lesion and disease recognition
		3.1 Design principles
			3.1.1 Choice of deep neural networks
			3.1.2 Choice of classification tasks and objectives
			3.1.3 Transfer learning
			3.1.4 Multitask learning
		3.2 Case study: skin disease classification versus skin lesion characterization
			3.2.1 Background
			3.2.2 Dataset
			3.2.3 Methodology
			3.2.4 Experiments
			3.2.5 Discussion
		3.3 Case study: skin lesion classification with multitask learning
			3.3.1 Background
			3.3.2 Dataset
			3.3.3 Methodology
			3.3.4 Experiments
			3.3.5 Discussion
		3.4 Summary
		References
	4 Detection: vertebrae localization and identification
		4.1 Design principles
			4.1.1 Choice of deep neural networks
			4.1.2 Choice of detection tasks and objectives
		4.2 Case study: vertebrae localization and identification
			4.2.1 Background
			4.2.2 Methodology
			4.2.3 Experiments
			4.2.4 Discussion
		4.3 Summary
		References
	5 Segmentation: intracardiac echocardiography contouring
		5.1 Design principles
			5.1.1 Choice of deep neural networks
			5.1.2 Choice of segmentation tasks and objectives
			5.1.3 Image restoration for segmentation
		5.2 Case study: intracardiac echocardiography contouring
			5.2.1 Methodology
			5.2.2 Experiments
			5.2.3 Discussion
		5.3 Summary
		References
	6 Registration: 2D/3D rigid registration
		6.1 Design principles
			6.1.1 Deep similarity based registration
				6.1.1.1 Problem definition and choice of objective functions
				6.1.1.2 Deep learning models for similarity metric learning
			6.1.2 Reinforcement learning based registration
				6.1.2.1 Problem definition and choice of objective functions
				6.1.2.2 Deep learning models for reinforcement learning based registration
			6.1.3 Supervised transformation estimation
				6.1.3.1 Problem definition and choice of objective functions
				6.1.3.2 Deep learning models for supervised transformation estimation
			6.1.4 Unsupervised transformation estimation
				6.1.4.1 Problem definition and choice of objective functions
				6.1.4.2 Deep learning models for unsupervised transformation estimation
		6.2 Case study: 2D/3D medical image registration
			6.2.1 Problem formulation
			6.2.2 Methodology
			6.2.3 Experiments
			6.2.4 Limitations
			6.2.5 Discussion
		6.3 Summary
		References
Part 2 Deep network design for medical image reconstruction, synthesis, and selected applications
	7 Reconstruction: supervised artifact reduction
		7.1 Design principles
			7.1.1 Image domain approaches
				7.1.1.1 Problem definition and choice of objective functions
				7.1.1.2 Deep learning models for image domain reconstruction
			7.1.2 Sensor domain approaches
				7.1.2.1 Problem definition and choice of objective functions
				7.1.2.2 Deep learning models for sensor domain reconstruction
			7.1.3 Dual-domain approaches
				7.1.3.1 Problem definition and choice of objective functions
				7.1.3.2 Deep learning models for dual-domain reconstruction
		7.2 Case study: sparse-view artifact reduction
			7.2.1 Background
			7.2.2 Methodology
				7.2.2.1 Network structure
				7.2.2.2 Focus map
			7.2.3 Experiments
				7.2.3.1 Dataset and models
				7.2.3.2 Results
			7.2.4 Discussion
		7.3 Case study: metal artifact reduction
			7.3.1 Background
				7.3.1.1 Inpainting-based methods
				7.3.1.2 MAR by iterative reconstruction
			7.3.2 Methodology
				7.3.2.1 Sinogram enhancement network
				7.3.2.2 Radon inversion layer
				7.3.2.3 Image enhancement network
			7.3.3 Experiments
				7.3.3.1 Ablation study
				7.3.3.2 Comparison with state-of-the-art methods
				7.3.3.3 Running time comparisons
			7.3.4 Discussion
		7.4 Summary
		References
	8 Reconstruction: unsupervised artifact reduction
		8.1 Design principles
			8.1.1 Unpaired learning approaches
				8.1.1.1 Problem definition and choice of objective functions
				8.1.1.2 Deep learning models for unpaired learning of medical image reconstruction
			8.1.2 Self-supervised learning approaches
				8.1.2.1 Problem definition and choice of objective functions
				8.1.2.2 Deep learning models for self-supervised learning of medical image reconstruction
		8.2 Case study: metal artifact reduction
			8.2.1 Background
			8.2.2 Methodology
				8.2.2.1 Encoders and decoders
				8.2.2.2 Learning
				8.2.2.3 Network architectures
			8.2.3 Experiments
				8.2.3.1 Baselines
				8.2.3.2 Datasets
				8.2.3.3 Training and testing
				8.2.3.4 Performance on synthesized data
				8.2.3.5 Performance on clinical data
				8.2.3.6 Ablation study
				8.2.3.7 Artifact synthesis
			8.2.4 Discussion
		8.3 Summary
		References
	9 Synthesis: novel radiography view synthesis
		9.1 Design principles
			9.1.1 Unconditional synthesis
				9.1.1.1 Problem definition and choice of objective functions
				9.1.1.2 Deep learning models for unconditional medical image synthesis
			9.1.2 Homogeneous domain synthesis
				9.1.2.1 Problem definition and choice of objective functions
				9.1.2.2 Deep learning models for homogeneous domain synthesis
			9.1.3 Heterogeneous domain synthesis
				9.1.3.1 Deep learning models for heterogeneous domain synthesis
		9.2 Case study: novel radiography view synthesis
			9.2.1 Background
				9.2.1.1 View synthesis from a single image
				9.2.1.2 Radiograph simulation and transformation to CT
			9.2.2 Methodology
				9.2.2.1 CT2Xray
				9.2.2.2 XraySyn
			9.2.3 Experiments
				9.2.3.1 Implementation details
				9.2.3.2 Dataset
				9.2.3.3 Evaluation metrics
				9.2.3.4 Ablation study
				9.2.3.5 Bone suppression
			9.2.4 Discussion
		9.3 Summary
		References
	10 Challenges and future directions
		10.1 Challenges and open issues
			10.1.1 Effectiveness in clinical workflows
			10.1.2 Responsible AI for healthcare
		10.2 Trends and future directions
		References
Index
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