Deep Learning in Medical Image Analysis: Challenges and Applications (Advances in Experimental Medicine and Biology, 1213)

Preface
Contents
Part I Overview and Issues
	Deep Learning in Medical Image Analysis
		Introduction
		Deep Learning for Medical Image Analysis and CAD
		Challenges in Deep-Learning-Based CAD
			Data Collection
			Transfer Learning
			Data Augmentation
			Training, Validation, and Independent Testing
			Acceptance Testing, Preclinical Testing, and User Training
			Quality Assurance and Performance Monitoring
			Interpretability of CAD/AI Recommendations
		Summary
	Medical Image Synthesis via Deep Learning
		Introduction
		Deep Learning Models for Medical Image Synthesis
			Convolutional Neural Networks
			Generative Adversarial Networks
		Within-Modality Synthesis
			3D cGAN
				Framework
				Experimental Results
			Locality Adaptive Multi-Modality GANs
				Framework
				Experimental Results
		Cross-Modality Synthesis
			3D cGAN with Subject-Specific Local Adaptive Fusion
				Framework
				Experimental Results
			Edge-Aware GANs
				Framework
				Experimental Results
		Conclusion
Part II Applications: Screening and Diagnosis
	Deep Learning for Pulmonary Image Analysis: Classification, Detection, and Segmentation
		Background of Lung Diseases
		Introduction
		Methods
			Classification of Lung Abnormalities
			Detection of Lung Abnormalities
			Segmentation of Lung Abnormalities
		Conclusion
	Deep Learning Computer-Aided Diagnosis for Breast Lesion in Digital Mammogram
		Introduction
		Related Work
		Materials and Methods
			Dataset
			Datasets Preparation: Training, Validation, and Testing
			Preprocessing
			Data Balancing and Augmentation
			Initialization of Trainable Parameters for Deep Learning Models
			Breast Lesion Detection via YOLO
			Breast Lesion Segmentation via FrCN
			Breast Lesion Classification via Three Convolutional Neural Networks
		Experimental Settings
			Detection Experimental Settings
			Segmentation Experimental Settings
			Classification Experimental Settings
			Implementation Environment
		Experimental Results and Discussion
			Evaluation Metrics
			Breast Lesion Detection Results
			Breast Lesion Segmentation Results
			Breast Lesion Classification Results
		Conclusion
	Decision Support System for Lung Cancer Using PET/CT and Microscopic Images
		Introduction
		Outline of Decision Support System
		Automated Detection of Lung Nodules in PET/CT Images Using Convolutional Neural Network and Radiomic Features
			Background
			Method Overview
			Initial Nodule Detection
			False Positive Reduction
				Classification Using a Convolutional Neural Network
				Handcrafted Radiomic Features
				Classification
			Results
				Image Datasets
				Evaluation Metrics
				Detection Results
			Discussion
		Automated Malignancy Analysis of Lung Nodules in PET/CT Images Using Radiomic Features
			Introduction
			Materials and Methods
				Image Dataset
				Methods Overview
				Volume of Interest (VOI) Extraction
				Extraction of Characteristic Features
				Classification
			Results
			Discussion
		Automated Malignancy Analysis Using Lung Cytological Images
			Introduction
			Materials and Methods
				Image Dataset
				Network Architecture
			Results and Discussion
		Automated Classification of Lung Cancer Types from Cytological Images
			Introduction
			Materials and Methods
				Image Dataset
				Network Architecture
			Results and Discussion
		Conclusion
	Lesion Image Synthesis Using DCGANs for Metastatic Liver Cancer Detection
		Introduction
		Proposed Method
			Dataset
			Lesion Image Generation
				Method 1: Synthesis Using Poisson Blending
				Method 2: Generation Based on a CT Value Distribution
				Method 3: Generation Using DCGANs
			Selection of the Region of Interest for Lesion Synthesis
			Detection Method
		Experiments
			Results
			Discussion
		Conclusion
	Retinopathy Analysis Based on Deep ConvolutionNeural Network
		Introduction
		General Arteriolar Narrowing Detection
			Blood Vessel Extraction
				Related Works
				Database
				Preprocessing
				Blood Vessel Extraction Using DCNN
			Detection of Arteriolar Narrowing Using AVR
				Related Works
				Database
				Classification of Arteries and Veins
				AVR Measurement
		Microaneurysm Detection
			Related Work
			Database
			Methods
				Preprocessing
				Microaneurysm Detection Based on DCNN
				Reducing the Number of False Positives
				Examination
		Conclusion
	Diagnosis of Glaucoma on Retinal Fundus Images Using Deep Learning: Detection of Nerve Fiber Layer Defect and Optic Disc Analysis
		Introduction
		Related Works
		NFLD Detection
			Background
			Proposed Method
				Segmentation Network
				Detection Network
				Combined Method
				Dataset
				Preprocessing
				Evaluation
			Results
			Discussion
		Optic Disc Analysis
			Background
			Methods
			Dataset
			Results
			Discussion
		Summary
Part III Applications: Emerging Opportunities
	Automatic Segmentation of Multiple Organs on 3D CT Images by Using Deep Learning Approaches
		Introduction
		Issue of Deep Learning for CT Image Segmentation
		Two Approaches for Multiple Organ Segmentations Using 2D and 3D Deep CNNs on CT Images
			Overview
			Deep Learning Anatomical Structures on 2D Sectional Images
			Deep Learning Local Appearances of Multiple Organs on 3D CT Images
			Conventional Image Segmentation Approach
		Results
		Discussions
			Segmentation Performances
			Training Protocol and Transfer Learning
			Comparison to Conventional Methods
			Computational Efficiency
		Conclusion
	Techniques and Applications in Skin OCT Analysis
		Introduction
		Skin Layer Segmentation in OCT
		Applications: Roughness, ET
		Deep Convolutional Networks in Skin Imaging
			Deep Learning for Classification of Dermoscopy Images
			Deep Learning for Classification of Full Field OCT Images
			Classification of Cross-Sectional OCT 2D Scans
			Semantic Segmentation in Cross-Sectional OCT Images
		Challenges
		Conclusions
	Deep Learning Technique for Musculoskeletal Analysis
		Importance of Musculoskeletal Analysis and Skeletal Muscle Analysis
		Musculoskeletal Recognition by Handcrafted Features and Its Limitations
		Skeletal Muscle Segmentation Using Deep Learning
		Whole-Body Muscle Analysis Using Deep Learning
		Fusion of Deep Learning and Handcrafted Features in Skeletal Muscle Modeling
		Conclusion
Index