Deep Learning Applications in Medical Image Segmentation (eBook, ePUB)

Overview, Approaches, and Challenges
Redaktion: Bhat, Sajid Yousuf; Abulaish, Muhammad; Rehman, Aasia

• Format: ePub

Produktbeschreibung

Apply revolutionary deep learning technology to the fast-growing field of medical image segmentation

Precise medical image segmentation is rapidly becoming one of the most important tools in medical research, diagnosis, and treatment. The potential for deep learning, a technology which is already revolutionizing practice across hundreds of subfields, is immense. The prospect of using deep learning to address the traditional shortcomings of image segmentation demands close inspection and wide proliferation of relevant knowledge.

Deep Learning Applications in Medical Image Segmentation meets this demand with a comprehensive introduction and its growing applications. Covering foundational concepts and its advanced techniques, it offers a one-stop resource for researchers and other readers looking for a detailed understanding of the topic. It is deeply engaged with the main challenges and recent advances in the field of deep-learning-based medical image segmentation.

Readers will also find:

• Analysis of deep learning models, including FCN, UNet, SegNet, Dee Lab, and many more
• Detailed discussion of medical image segmentation divided by area, incorporating all major organs and organ systems
• Recent deep learning advancements in segmenting brain tumors, retinal vessels, and inner ear structures
• Analyzes the effectiveness of deep learning models in segmenting lung fields for respiratory disease diagnosis
• Explores the application and benefits of Generative Adversarial Networks (GANs) in enhancing medical image segmentation
• Identifies and discusses the key challenges faced in medical image segmentation using deep learning techniques
• Provides an overview of the latest advancements, applications, and future trends in deep learning for medical image analysis

Deep Learning Applications in Medical Image Segmentation is ideal for academics and researchers working with medical image segmentation, as well as professionals in medical imaging, data science, and biomedical engineering.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 490
• Erscheinungstermin: 3. Januar 2025
• Englisch
• ISBN-13: 9781394245345
• Artikelnr.: 72738968

Autorenporträt

Sajid Yousuf Bhat, PhD, is an Assistant Professor in the Department of Computer Science, University of Kashmir, Srinagar, India. Dr. Bhat received his PhD in Computer Science from Jamia Millia Islamia, India, in 2014. His current areas of research include image analysis, machine learning, network analysis and business intelligence. Aasia Rehman, PhD, is a Lecturer in the Department of Computer Science, University of Kashmir, Srinagar, India. Dr. Rehman earned her PhD in Computer Science from the University of Kashmir, India, in 2023. Her current research area includes medical image segmentation, image classification and deep learning. Muhammad Abulaish, PhD, is a Professor in the Department of Computer Science, South Asian University, New Delhi, India. Professor Abulaish earned his PhD in Computer Science from the Indian Institute of Technology Delhi, India, in 2007. His research focuses on the development of innovative data mining, machine learning, and network analysis techniques to address real-world societal and industrial problems.

Inhaltsangabe

Acknowledgments xv
List of Contributors xvii
Preface xxi
Introduction xxii
1 Introduction to Medical Image Segmentation: Overview of Modalities,
Benchmark Datasets, Data Augmentation Techniques, and Evaluation Metrics 1
Aasia Rehman and Suhail Qadir Mir
1.1 Introduction 1
1.1.1 X-Rays 1
1.1.2 Computed Tomography (CT) 3
1.1.3 Medical Resonance Imaging (MRI) 4
1.1.4 Positron Emission Tomography (PET) 5
1.1.5 Ultrasound (US) Images 6
1.1.6 Colonoscopy 7
1.1.7 Dermoscopy 8
1.1.8 Microscopic Images 8
1.1.9 Optical Coherence Tomography (OCT) 9
1.2 Datasets for Segmentation of Medical Images 10
1.2.1 Multimodal Brain Tumor Segmentation Challenge (BraTS) Dataset 11
1.2.2 LIDC-IDRI (Lung Image Database Consortium Image Collection) Dataset
11
1.2.3 LiTS (Liver Tumor Segmentation) Dataset 12
1.2.4 KiTS (Kidney Tumor Segmentation) Dataset 12
1.2.5 ISIC (International Skin Imaging Collaboration) Dataset 13
1.2.6 BUSI (Breast Ultrasound) Dataset 13
1.2.7 Colonoscopy Datasets 14
1.2.7.1 Kvasir-SEG 14
1.2.7.2 CVC-ClinicDB and CVC-ColonDB 15
1.3 Augmentation Techniques Used in Medical Image Segmentation 15
1.4 Performance Metrics for Evaluating Segmentation Models 20
1.4.1 Dice Similarity Coefficient (DSC) 20
1.4.2 Intersection over Union (IoU) 21
1.4.3 Precision 21
1.4.4 Recall 21
1.4.5 F1 Score 22
1.4.6 Accuracy 22
1.5 Conclusion 22
References 23
2 Fundamentals of Deep Learning Models for Medical Image Segmentation 27
Aasia Rehman and Sajid Yousuf Bhat
2.1 Introduction 27
2.2 Deep Learning Models for Medical Image Segmentation 28
2.2.1 Convolutional Neural Network (CNN) 28
2.2.2 Fully Convolutional Neural Network (FCN) 29
2.2.3 UNet 32
2.2.4 Multi-scale-Based Models 34
2.2.5 Pyramid-Based Models 34
2.2.6 Recurrent Neural Networks (RNNs) 35
2.2.7 Attention-Based Models 36
2.2.8 Ensemble-Based Models 37
2.2.9 Other Models 37
2.3 Applications of Medical Image Segmentation Models 38
2.3.1 Segmentation of Anatomical Organs 39
2.3.1.1 Eye 39
2.3.1.2 Brain 40
2.3.1.3 Liver 42
2.3.1.4 Lung 44
2.3.1.5 Kidney 46
2.3.1.6 Heart 47
2.3.1.7 Multi-organ 49
2.4 Current Challenges in Segmentation of Medical Images 50
2.5 Conclusion 52
References 52
3 Revealing Historical Insights: A Comprehensive Exploration of Traditional
Approaches in Medical Image Segmentation 65
Mudasir Ashraf and Majid Zaman
3.1 Introduction 65
3.1.1 Contextualizing Medical Image Segmentation 65
3.1.2 The Significance of Accurate Segmentation 66
3.1.3 Traditional Approaches in Medical Image Segmentation 66
3.1.4 Evolution Over Time 66
3.1.5 Aims of the Research 66
3.2 Literature Review 67
3.2.1 Historical Evolution of Medical Image Segmentation 67
3.2.2 Thresholding Techniques and Their Applications 69
3.2.3 Region-Based Techniques 69
3.2.4 Contour-Based Algorithms and Their Applications 69
3.2.5 Limitations of Traditional Approaches 70
3.2.6 The Rise of Deep Learning in Medical Image Segmentation 70
3.2.7 Transfer Learning and Multi-modal Information 70
3.2.8 Real-Time Segmentation and Clinical Applications 70
3.2.9 Challenges and Opportunities in the Modern Era 71
3.2.10 Future Directions and Research Implications 71
3.3 Methodology 72
3.3.1 Explanation of Traditional Approaches 72
3.3.1.1 Thresholding 72
3.3.1.2 Region-Based Techniques 72
3.3.1.3 Contour-Based Algorithms 72
3.3.2 Application in Medical Image Segmentation 72
3.3.2.1 Thresholding Applications 72
3.3.2.2 Region-Based Techniques in Practice 72
3.3.2.3 Contour-Based Algorithm Applications 73
3.3.3 Datasets and Tools 73
3.3.4 Integration of Text Reports and Medical Image Data 73
3.4 Historical Context 74
3.4.1 Early Heuristic Approaches 74
3.4.2 Emergence of Thresholding 74
3.4.3 Rise of Region-Based Techniques 74
3.4.4 Introduction of Contour-Based Algorithms 74
3.4.5 Computational Advancements and Modern Era 75
3.5 Segmentation 75
3.6 Challenges and Opportunities 76
3.6.1 Challenges in Traditional Approaches 76
3.6.2 Limitations in Handling Modern Imaging Modalities 77
3.6.3 Bridging the Gap: Integrating Computational Techniques 77
3.6.4 Leveraging Big Data and Real-World Applications 77
3.7 Case Studies 77
3.7.1 Application of Traditional Approaches in Clinical Settings 77
3.7.2 Challenges Encountered in Real-World Scenarios 78
3.7.3 Integration of Computational Techniques 78
3.7.4 Leveraging Big Data for Improved Segmentation 78
3.8 Modern Era and Contemporary Techniques 78
3.8.1 Evolution Beyond Traditional Approaches 78
3.8.2 Role of Deep Learning in Medical Image Segmentation 79
3.8.3 Transfer Learning and Generalization 79
3.8.4 Integration of Multi-modal Information 79
3.8.5 Real-Time Segmentation and Clinical Applications 80
3.8.6 Challenges and Ongoing Research 80
3.9 Conclusion 80
References 81
4 Segmentation and Quantitative Analysis of Myelinated White Matter Tissue
in Pediatric Brain Magnetic Resonance Images 85
Chelli N. Devi, Anupama Chandrasekharan, V. K. Sundararaman, and Zachariah
C. Alex
4.1 Introduction 85
4.2 Literature Review 88
4.2.1 Segmentation of MWM in the Pediatric Brain 88
4.2.2 Qualitative and Quantitative Study of Myelination 89
4.3 Methodology 91
4.3.1 Input Datasets 91
4.3.2 Pediatric Brain Extraction and Myelin Segmentation 91
4.3.3 Myelin Visualization, Computation of Myelin Index, and Growth Model
Fitting 93
4.3.4 Study of Hemispheric Differences in Myelination 96
4.3.5 Study of Myelination in Premature Babies 97
4.4 Results 98
4.4.1 Brain Extraction, Myelin Segmentation, and 3D Visualization 98
4.4.2 Growth Model Fitting 98
4.4.3 Myelination in Right and Left Hemispheres 102
4.4.4 Myelination in Premature Babies 103
4.5 Discussion 103
4.5.1 Clinical Significance of the Study of Myelination 103
4.5.2 Modeling Myelination in Neonates, Infants, and Children 104
4.5.3 Hemispheric Differences in Myelination 106
4.5.4 Preterm Myelination 107
4.6 Conclusion 107
References 108
5 Deep Learning Transformations in Medical Imaging: Advancements in Brain
Tumor, Retinal Vessel, and Inner Ear Segmentation 113
Rejaul Karim Barbhuiya and Chayan Paul
5.1 Introduction 113
5.2 Classical Image Segmentation Techniques 116
5.2.1 Thresholding 116
5.2.2 Region Growing 117
5.2.3 Edge Detection 117
5.2.4 Clustering 117
5.2.5 Watershed Transform 117
5.3 Deep Learning-Based Image Segmentation Methods for Medical Images 118
5.3.1 Convolutional Neural Network (CNN) 119
5.3.1.1 Convolutional Layer 119
5.3.1.2 Activation Function 120
5.3.1.3 Pooling Layers 120
5.3.1.4 Fully Connected Layers 120
5.3.1.5 Softmax Activation 121
5.3.1.6 Loss Function and Optimization 121
5.3.1.7 Backpropagation 123
5.3.2 U-Net 123
5.3.3 GoogleNet or Inception 124
5.4 Deep Learning Algorithms Employed in the Segmentation of Brain Tumor
Images 125
5.5 Deep Learning Models for Retinal Vessel Segmentation 126
5.6 Deep Learning Models for Inner Ear Segmentation 128
5.7 Conclusion 129
References 130
6 Deep Learning-Based Image Segmentation for Early Detection of Diabetic
Retinopathy and Other Retinal Disorders 133
Ankur Biswas and Rita Banik
6.1 Introduction 133
6.2 Deep Learning and Image Segmentation 135
6.2.1 Convolutional Neural Networks Architecture 136
6.2.2 Pretrained Models and Transfer Learning 136
6.2.3 Other Deep Learning Techniques 137
6.3 Applications and Benefits of Deep Learning-Based Image Segmentation 138
6.3.1 Detection of Diabetic Retinopathy 138
6.3.2 Determination of Additional Retinal Conditions 139
6.3.2.1 Optic Disc 139
6.3.2.2 Microaneurysm 139
6.3.2.3 Hemorrhage 140
6.3.2.4 Hard Exudates 140
6.3.2.5 Soft Exudates (Cotton-Wool Spots) 140
6.3.2.6 Retinal Vessel 141
6.3.3 Monitoring the Progression of the Disease and Quantitative Analysis
141
6.3.4 Healthcare Professionals' Assistive Tool 142
6.4 Challenges and Limitations 142
6.4.1 Dataset Quality and Availability 144
6.4.2 Explainability and Interpretability of the Model 144
6.4.3 Complexity of Computation and Necessity for Resources 144
6.4.4 Ethical Issues 145
6.5 Conclusions and Future Directions 145
References 146
7 Analysis of Deep Learning Models for Lung Field Segmentation 149
Tairah Andrabi and Sajid Yousuf Bhat
7.1 Introduction 149
7.2 Medical Imaging Modalities 151
7.3 Overview of Classical Approaches for Lung Segmentation in Chest X-rays
152
7.3.1 Rule-Based Methods 152
7.3.2 Deformable Methods 153
7.3.3 Parametric Methods 154
7.3.4 Geometric Deformable Models 154
7.3.5 Pixel Classifier-Based Segmentation 155
7.3.6 Shallow Learning 156
7.4 Deep Learning Approaches 156
7.4.1 Lung Field Segmentation in Chest X-rays 156
7.4.1.1 CNN-Based Approaches 157
7.4.1.2 U-Net-Based Approaches 158
7.4.1.3 Dilated Convolution-Based Approaches 160
7.4.1.4 Attention-Based Approaches 161
7.4.1.5 GAN-Based Approaches 162
7.4.1.6 Multistage and Ensemble Approaches 163
7.4.2 Overview of Deep Learning Approaches for Lung Segmentation in CT
Scans 164
7.5 Data Sources and Datasets 166
7.5.1 Chest X-Ray Datasets 169
7.5.1.1 JSRT ("Japanese Society of Radiological Technology") 169
7.5.1.2 Montgomery County (MC) Dataset 169
7.5.1.3 Shenzhen Dataset 169
7.5.1.4 Indian Dataset 170
7.5.1.5 National Institutes of Health Chest X-Ray Dataset (NIH) 170
7.5.1.6 COVID-19 Radiography Database 171
7.5.2 CT Scan Datasets 172
7.6 Evaluation Metrics 173
7.7 Conclusion 176
References 176
8 Generative Adversarial Networks in the Field of Medical Image
Segmentation 185
Bisma Sultan, Aasia Rehman, and Lubna Riyaz
8.1 Introduction 185
8.2 Overview of Image Segmentation Techniques 187
8.2.1 Traditional Methods for Segmentation of Images 187
8.2.2 Deep Learning Methods for Segmentation of Images 189
8.3 Generative Adversarial Networks 190
8.3.1 Vanilla GAN 190
8.3.1.1 GAN Framework 191
8.3.2 GAN Variants 192
8.3.2.1 InfoGAN 192
8.3.2.2 Dcgan 193
8.3.2.3 cGAN 194
8.3.2.4 Acgan 194
8.3.2.5 Wgan 194
8.4 Classification of GAN-Based Image Segmentation Techniques 195
8.4.1 Classification on the Basis of Segmentation Area 195
8.4.1.1 Brain Segmentation Using GAN 195
8.4.1.2 Eye Segmentation Using GAN 197
8.4.1.3 Cardiology Segmentation Using GAN 198
8.4.1.4 Chest Segmentation Using GAN 199
8.4.1.5 Breast Segmentation Using GANs 200
8.4.1.6 Spine Segmentation Using GANs 200
8.4.1.7 Abdomen Segmentation Using GANs 202
8.4.1.8 Pelvic Segmentation Using GANs 202
8.4.2 Classification on the Basis of Image Modality 203
8.4.2.1 Segmentation of Magnetic Resonance Imaging (MRI) Using GAN 203
8.4.2.2 Segmentation of Computed Tomography (CT) Images Using Gan 203
8.4.2.3 Segmentation of Other Modalities Using GAN 205
8.4.3 Classification on the Basis of GAN Model Employed 206
8.4.3.1 Segmentation Using U-Net Based GAN 207
8.4.3.2 Segmentation Using Conditional GANs (CGAN, pix2pix GAN, and Acgan)
207
8.4.3.3 Segmentation Using CycleGAN 208
8.4.3.4 Segmentation Using other GAN Models 210
8.5 Conclusion 210
References 212
9 A Collaborative Cell Image Segmentation Model Based on the Multilevel
Improvement of Data 227
Ishfaq Sheikh, Manzoor Chachoo, and Aijaz Mir
9.1 Introduction 227
9.2 Methodology 229
9.3 Result and Discussion 234
9.4 Conclusion and Future Scope 238
Acknowledgments 240
References 240
10 Challenges and Future Directions for Segmentation of Medical Images
Using Deep Learning Models 243
Roshan Birjais
10.1 Introduction 243
10.2 Types of Medical Datasets 244
10.2.1 X-Ray 244
10.2.2 Computerized Tomography 246
10.2.3 Mammography (MG) 247
10.2.4 Histopathology 247
10.2.5 Magnetic Resonance Imaging (MRI) 249
10.2.6 Other Images 250
10.3 Challenges Related to the Dataset 250
10.3.1 Limited Annotated Dataset 252
10.3.1.1 Solution 253
10.3.2 Sparse Annotations 254
10.3.2.1 Solution 254
10.3.3 Class Imbalance in Datasets 254
10.3.3.1 Solution 254
10.3.4 Intensity Inhomogeneities 255
10.3.4.1 Solution 255
10.3.5 Complexities in Image Texture 255
10.3.5.1 Solution 256
10.4 Challenges Concerning the DL Models 256
10.4.1 Overfitting 256
10.4.1.1 Solution 257
10.4.2 Space Complexity of Models 257
10.4.2.1 Solution 257
10.4.2.2 Solution 257
10.4.3 Vanishing and Exploding Gradient 257
10.4.3.1 Solution 258
10.4.4 Computational Complexity 258
10.4.4.1 Solution 258
10.5 Conclusion 258
References 259
11 Advancements in Deep Learning for Medical Image Analysis: A
Comprehensive Exploration of Techniques, Applications, and Future Prospects
265
Vani Malagar, Navin Mani Upadhyay, and Mekhla Sharma
11.1 Introduction 265
11.2 Significance of Medical Image Segmentation 266
11.2.1 Case Studies 267
11.3 Deep Learning Techniques for Medical Image Segmentation 268
11.3.1 Building Blocks for Medical Image Segmentation 268
11.3.1.1 Activation Functions 269
11.3.1.2 Loss Functions 269
11.3.1.3 Gradient Descent with Backpropagation 270
11.3.2 Common Architectures 270
11.3.2.1 U-Net 270
11.3.2.2 Components 270
11.3.2.3 Convolutional Neural Networks (CNNs) 271
11.3.2.4 Fully Convolutional Network (FCN) 271
11.3.2.5 SegNet 271
11.3.2.6 DeepLabv3+ 273
11.3.3 Advanced Techniques 273
11.3.3.1 Generative Adversarial Networks (GANs) for Data Augmentation 273
11.3.3.2 Transformers for Medical Image Segmentation 274
11.3.3.3 Ensemble Learning for Improved Performance 274
11.4 Applications of Deep Learning in Medical Image Segmentation 275
11.4.1 Diagnostic Applications 275
11.4.1.1 Improved Tumor Segmentation and Characterization 275
11.4.1.2 Lesion Detection and Analysis 276
11.4.1.3 Organ Segmentation 276
11.4.1.4 Medical Image Analysis for Disease Progression Monitoring 277
11.4.2 Therapeutic Applications 277
11.5 Challenges and Future Prospects 277
11.5.1 Challenges and Limitations 277
11.5.2 Future Trends and Advancements 280
11.5.2.1 Self-Supervised Learning 280
11.5.2.2 Explainable AI (XAI) Techniques 280
11.5.2.3 Federated Learning 282
11.5.2.4 Integration with AI for Comprehensive Medical Decision Support
Systems 283
11.6 Conclusion 283
References 284
Index 285