Deep Learning in Medical Image Analysis (eBook)

Challenges and Applications

Gobert Lee, Hiroshi Fujita (Herausgeber)

eBook Download: PDF

2020 | 1st ed. 2020
VIII, 181 Seiten
Springer International Publishing (Verlag)
978-3-030-33128-3 (ISBN)

Infos

This book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems. Each of its chapters covers a topic in depth, ranging from medical image synthesis and techniques for muskuloskeletal analysis to diagnostic tools for breast lesions on digital mammograms and glaucoma on retinal fundus images. It also provides an overview of deep learning in medical image analysis and highlights issues and challenges encountered by researchers and clinicians, surveying and discussing practical approaches in general and in the context of specific problems. Academics, clinical and industry researchers, as well as young researchers and graduate students in medical imaging, computer-aided-diagnosis, biomedical engineering and computer vision will find this book a great reference and very useful learning resource.

Gobert Lee is a lecturer in Statistical Science and the Director of Studies in Mathematics and Statistics at the College of Science and Engineering, and a research member of the Medical Device Research Institute, Flinders University, Adelaide, Australia. Gobert's research interests include statistical pattern recognition, medical image segmentation, computer-aided-diagnosis systems, breast cancer detection and analysis, multi-organ CT segmentation and human voxel model generation.

Hiroshi Fujita is a Research Professor/Emeritus Professor of Gifu University. He is a member of the Society for Medical Image Information (president), the Research Group on Medical Imaging (adviser), the Japan Society for Medical Image Engineering (director), and some other societies. His research interests include computer-aided diagnosis system, image analysis and processing, and image evaluation in medicine. He has published over 1000 papers in Journals, Proceedings, Book chapters and Scientific Magazines.

Inhalt

Preface 6
Contents 8
Part I Overview and Issues 10
Deep Learning in Medical Image Analysis 11
Introduction 11
Deep Learning for Medical Image Analysis and CAD 12
Challenges in Deep-Learning-Based CAD 15
Data Collection 17
Transfer Learning 19
Data Augmentation 23
Training, Validation, and Independent Testing 24
Acceptance Testing, Preclinical Testing, and User Training 24
Quality Assurance and Performance Monitoring 25
Interpretability of CAD/AI Recommendations 26
Summary 26
Medical Image Synthesis via Deep Learning 30
Introduction 30
Deep Learning Models for Medical Image Synthesis 33
Convolutional Neural Networks 33
Generative Adversarial Networks 34
Within-Modality Synthesis 35
3D cGAN 36
Framework 36
Experimental Results 36
Locality Adaptive Multi-Modality GANs 38
Framework 39
Experimental Results 40
Cross-Modality Synthesis 41
3D cGAN with Subject-Specific Local Adaptive Fusion 41
Framework 42
Experimental Results 43
Edge-Aware GANs 43
Framework 44
Experimental Results 46
Conclusion 47
Part II Applications: Screening and Diagnosis 52
Deep Learning for Pulmonary Image Analysis: Classification, Detection, and Segmentation 53
Background of Lung Diseases 53
Introduction 54
Methods 54
Classification of Lung Abnormalities 54
Detection of Lung Abnormalities 58
Segmentation of Lung Abnormalities 59
Conclusion 62
Deep Learning Computer-Aided Diagnosis for Breast Lesion in Digital Mammogram 65
Introduction 66
Related Work 67
Materials and Methods 68
Dataset 68
Datasets Preparation: Training, Validation, and Testing 69
Preprocessing 69
Data Balancing and Augmentation 69
Initialization of Trainable Parameters for Deep Learning Models 70
Breast Lesion Detection via YOLO 70
Breast Lesion Segmentation via FrCN 70
Breast Lesion Classification via Three Convolutional Neural Networks 71
Experimental Settings 72
Detection Experimental Settings 72
Segmentation Experimental Settings 72
Classification Experimental Settings 72
Implementation Environment 73
Experimental Results and Discussion 73
Evaluation Metrics 73
Breast Lesion Detection Results 73
Breast Lesion Segmentation Results 73
Breast Lesion Classification Results 75
Conclusion 76
Decision Support System for Lung Cancer Using PET/CT and Microscopic Images 79
Introduction 79
Outline of Decision Support System 80
Automated Detection of Lung Nodules in PET/CT Images Using Convolutional Neural Network and Radiomic Features 81
Background 81
Method Overview 81
Initial Nodule Detection 82
False Positive Reduction 82
Classification Using a Convolutional Neural Network 83
Handcrafted Radiomic Features 83
Classification 83
Results 84
Image Datasets 84
Evaluation Metrics 84
Detection Results 84
Discussion 85
Automated Malignancy Analysis of Lung Nodules in PET/CT Images Using Radiomic Features 86
Introduction 86
Materials and Methods 86
Image Dataset 86
Methods Overview 87
Volume of Interest (VOI) Extraction 87
Extraction of Characteristic Features 87
Classification 90
Results 90
Discussion 90
Automated Malignancy Analysis Using Lung Cytological Images 92
Introduction 92
Materials and Methods 93
Image Dataset 93
Network Architecture 93
Results and Discussion 94
Automated Classification of Lung Cancer Types from Cytological Images 94
Introduction 94
Materials and Methods 95
Image Dataset 95
Network Architecture 96
Results and Discussion 96
Conclusion 98
Lesion Image Synthesis Using DCGANs for Metastatic Liver Cancer Detection 101
Introduction 101
Proposed Method 103
Dataset 103
Lesion Image Generation 103
Method 1: Synthesis Using Poisson Blending 103
Method 2: Generation Based on a CT Value Distribution 104
Method 3: Generation Using DCGANs 105
Selection of the Region of Interest for Lesion Synthesis 105
Detection Method 107
Experiments 108
Results 109
Discussion 109
Conclusion 110
Retinopathy Analysis Based on Deep ConvolutionNeural Network 113
Introduction 113
General Arteriolar Narrowing Detection 114
Blood Vessel Extraction 114
Related Works 114
Database 115
Preprocessing 115
Blood Vessel Extraction Using DCNN 116
Detection of Arteriolar Narrowing Using AVR 117
Related Works 117
Database 118
Classification of Arteries and Veins 118
AVR Measurement 119
Microaneurysm Detection 120
Related Work 120
Database 121
Methods 121
Preprocessing 121
Microaneurysm Detection Based on DCNN 122
Reducing the Number of False Positives 123
Examination 124
Conclusion 124
Diagnosis of Glaucoma on Retinal Fundus Images Using Deep Learning: Detection of Nerve Fiber Layer Defect and Optic Disc Analysis 127
Introduction 127
Related Works 129
NFLD Detection 129
Background 129
Proposed Method 130
Segmentation Network 130
Detection Network 131
Combined Method 131
Dataset 131
Preprocessing 133
Evaluation 133
Results 133
Discussion 133
Optic Disc Analysis 135
Background 135
Methods 135
Dataset 136
Results 136
Discussion 136
Summary 137
Part III Applications: Emerging Opportunities 139
Automatic Segmentation of Multiple Organs on 3D CT Images by Using Deep Learning Approaches 140
Introduction 141
Issue of Deep Learning for CT Image Segmentation 141
Two Approaches for Multiple Organ Segmentations Using 2D and 3D Deep CNNs on CT Images 142
Overview 142
Deep Learning Anatomical Structures on 2D Sectional Images 142
Deep Learning Local Appearances of Multiple Organs on 3D CT Images 143
Conventional Image Segmentation Approach 145
Results 145
Discussions 147
Segmentation Performances 147
Training Protocol and Transfer Learning 148
Comparison to Conventional Methods 149
Computational Efficiency 150
Conclusion 151
Techniques and Applications in Skin OCT Analysis 153
Introduction 154
Skin Layer Segmentation in OCT 154
Applications: Roughness, ET 160
Deep Convolutional Networks in Skin Imaging 161
Deep Learning for Classification of Dermoscopy Images 162
Deep Learning for Classification of Full Field OCT Images 162
Classification of Cross-Sectional OCT 2D Scans 162
Semantic Segmentation in Cross-Sectional OCT Images 164
Challenges 164
Conclusions 165
Deep Learning Technique for Musculoskeletal Analysis 168
Importance of Musculoskeletal Analysis and Skeletal Muscle Analysis 168
Musculoskeletal Recognition by Handcrafted Features and Its Limitations 169
Skeletal Muscle Segmentation Using Deep Learning 170
Whole-Body Muscle Analysis Using Deep Learning 174
Fusion of Deep Learning and Handcrafted Features in Skeletal Muscle Modeling 176
Conclusion 177
Index 180

Produkt-Details

• Erscheint lt. Verlag: 6.2.2020
• Reihe/Serie: Advances in Experimental Medicine and Biology
• Reihe/Serie: Advances in Experimental Medicine and Biology
• Zusatzinfo: VIII, 181 p. 131 illus., 114 illus. in color.
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik
• Themenwelt: Medizin / Pharmazie ► Allgemeines / Lexika
• Themenwelt: Studium ► 1. Studienabschnitt (Vorklinik) ► Biochemie / Molekularbiologie
• Themenwelt: Naturwissenschaften ► Biologie ► Genetik / Molekularbiologie
• Schlagworte: Breast Cancer Detection • computer aided dianosis • convolutional neural network • Deep learning • Deep Neural Network • lung nodule detection • Medical Image Analysis • multi organ segmentation • Pulmonary Image Analysis • Retinopathy
• ISBN-10: 3-030-33128-8 / 3030331288
• ISBN-13: 978-3-030-33128-3 / 9783030331283