Deep Learning Applications in Medical Image Segmentation

Overview, Approaches, and Challenges

Inbunden, Engelska, 2025

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Beskrivning

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 moreDetailed discussion of medical image segmentation divided by area, incorporating all major organs and organ systemsRecent deep learning advancements in segmenting brain tumors, retinal vessels, and inner ear structuresAnalyzes the effectiveness of deep learning models in segmenting lung fields for respiratory disease diagnosisExplores the application and benefits of Generative Adversarial Networks (GANs) in enhancing medical image segmentationIdentifies and discusses the key challenges faced in medical image segmentation using deep learning techniquesProvides an overview of the latest advancements, applications, and future trends in deep learning for medical image analysisDeep 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.

Produktinformation

Utgivningsdatum:2025-01-02
Mått:160 x 237 x 27 mm
Vikt:694 g
Format:Inbunden
Språk:Engelska
Antal sidor:320
Förlag:John Wiley & Sons Inc
ISBN:9781394245338

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Biokemisk teknik inom Naturvetenskap och teknik
Biomedicinsk teknik inom Medicin

Mer om författaren

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.

Innehållsförteckning

Acknowledgments xvList of Contributors xviiPreface xxiIntroduction xxii1 Introduction to Medical Image Segmentation: Overview of Modalities, Benchmark Datasets, Data Augmentation Techniques, and Evaluation Metrics 1Aasia Rehman and Suhail Qadir Mir1.1 Introduction 11.1.1 X-Rays 11.1.2 Computed Tomography (CT) 31.1.3 Medical Resonance Imaging (MRI) 41.1.4 Positron Emission Tomography (PET) 51.1.5 Ultrasound (US) Images 61.1.6 Colonoscopy 71.1.7 Dermoscopy 81.1.8 Microscopic Images 81.1.9 Optical Coherence Tomography (OCT) 91.2 Datasets for Segmentation of Medical Images 101.2.1 Multimodal Brain Tumor Segmentation Challenge (BraTS) Dataset 111.2.2 LIDC-IDRI (Lung Image Database Consortium Image Collection) Dataset 111.2.3 LiTS (Liver Tumor Segmentation) Dataset 121.2.4 KiTS (Kidney Tumor Segmentation) Dataset 121.2.5 ISIC (International Skin Imaging Collaboration) Dataset 131.2.6 BUSI (Breast Ultrasound) Dataset 131.2.7 Colonoscopy Datasets 141.2.7.1 Kvasir-SEG 141.2.7.2 CVC-ClinicDB and CVC-ColonDB 151.3 Augmentation Techniques Used in Medical Image Segmentation 151.4 Performance Metrics for Evaluating Segmentation Models 201.4.1 Dice Similarity Coefficient (DSC) 201.4.2 Intersection over Union (IoU) 211.4.3 Precision 211.4.4 Recall 211.4.5 F1 Score 221.4.6 Accuracy 221.5 Conclusion 22References 232 Fundamentals of Deep Learning Models for Medical Image Segmentation 27Aasia Rehman and Sajid Yousuf Bhat2.1 Introduction 272.2 Deep Learning Models for Medical Image Segmentation 282.2.1 Convolutional Neural Network (CNN) 282.2.2 Fully Convolutional Neural Network (FCN) 292.2.3 UNet 322.2.4 Multi-scale-Based Models 342.2.5 Pyramid-Based Models 342.2.6 Recurrent Neural Networks (RNNs) 352.2.7 Attention-Based Models 362.2.8 Ensemble-Based Models 372.2.9 Other Models 372.3 Applications of Medical Image Segmentation Models 382.3.1 Segmentation of Anatomical Organs 392.3.1.1 Eye 392.3.1.2 Brain 402.3.1.3 Liver 422.3.1.4 Lung 442.3.1.5 Kidney 462.3.1.6 Heart 472.3.1.7 Multi-organ 492.4 Current Challenges in Segmentation of Medical Images 502.5 Conclusion 52References 523 Revealing Historical Insights: A Comprehensive Exploration of Traditional Approaches in Medical Image Segmentation 65Mudasir Ashraf and Majid Zaman3.1 Introduction 653.1.1 Contextualizing Medical Image Segmentation 653.1.2 The Significance of Accurate Segmentation 663.1.3 Traditional Approaches in Medical Image Segmentation 663.1.4 Evolution Over Time 663.1.5 Aims of the Research 663.2 Literature Review 673.2.1 Historical Evolution of Medical Image Segmentation 673.2.2 Thresholding Techniques and Their Applications 693.2.3 Region-Based Techniques 693.2.4 Contour-Based Algorithms and Their Applications 693.2.5 Limitations of Traditional Approaches 703.2.6 The Rise of Deep Learning in Medical Image Segmentation 703.2.7 Transfer Learning and Multi-modal Information 703.2.8 Real-Time Segmentation and Clinical Applications 703.2.9 Challenges and Opportunities in the Modern Era 713.2.10 Future Directions and Research Implications 713.3 Methodology 723.3.1 Explanation of Traditional Approaches 723.3.1.1 Thresholding 723.3.1.2 Region-Based Techniques 723.3.1.3 Contour-Based Algorithms 723.3.2 Application in Medical Image Segmentation 723.3.2.1 Thresholding Applications 723.3.2.2 Region-Based Techniques in Practice 723.3.2.3 Contour-Based Algorithm Applications 733.3.3 Datasets and Tools 733.3.4 Integration of Text Reports and Medical Image Data 733.4 Historical Context 743.4.1 Early Heuristic Approaches 743.4.2 Emergence of Thresholding 743.4.3 Rise of Region-Based Techniques 743.4.4 Introduction of Contour-Based Algorithms 743.4.5 Computational Advancements and Modern Era 753.5 Segmentation 753.6 Challenges and Opportunities 763.6.1 Challenges in Traditional Approaches 763.6.2 Limitations in Handling Modern Imaging Modalities 773.6.3 Bridging the Gap: Integrating Computational Techniques 773.6.4 Leveraging Big Data and Real-World Applications 773.7 Case Studies 773.7.1 Application of Traditional Approaches in Clinical Settings 773.7.2 Challenges Encountered in Real-World Scenarios 783.7.3 Integration of Computational Techniques 783.7.4 Leveraging Big Data for Improved Segmentation 783.8 Modern Era and Contemporary Techniques 783.8.1 Evolution Beyond Traditional Approaches 783.8.2 Role of Deep Learning in Medical Image Segmentation 793.8.3 Transfer Learning and Generalization 793.8.4 Integration of Multi-modal Information 793.8.5 Real-Time Segmentation and Clinical Applications 803.8.6 Challenges and Ongoing Research 803.9 Conclusion 80References 814 Segmentation and Quantitative Analysis of Myelinated White Matter Tissue in Pediatric Brain Magnetic Resonance Images 85Chelli N. Devi, Anupama Chandrasekharan, V. K. Sundararaman, and Zachariah C. Alex4.1 Introduction 854.2 Literature Review 884.2.1 Segmentation of MWM in the Pediatric Brain 884.2.2 Qualitative and Quantitative Study of Myelination 894.3 Methodology 914.3.1 Input Datasets 914.3.2 Pediatric Brain Extraction and Myelin Segmentation 914.3.3 Myelin Visualization, Computation of Myelin Index, and Growth Model Fitting 934.3.4 Study of Hemispheric Differences in Myelination 964.3.5 Study of Myelination in Premature Babies 974.4 Results 984.4.1 Brain Extraction, Myelin Segmentation, and 3D Visualization 984.4.2 Growth Model Fitting 984.4.3 Myelination in Right and Left Hemispheres 1024.4.4 Myelination in Premature Babies 1034.5 Discussion 1034.5.1 Clinical Significance of the Study of Myelination 1034.5.2 Modeling Myelination in Neonates, Infants, and Children 1044.5.3 Hemispheric Differences in Myelination 1064.5.4 Preterm Myelination 1074.6 Conclusion 107References 1085 Deep Learning Transformations in Medical Imaging: Advancements in Brain Tumor, Retinal Vessel, and Inner Ear Segmentation 113Rejaul Karim Barbhuiya and Chayan Paul5.1 Introduction 1135.2 Classical Image Segmentation Techniques 1165.2.1 Thresholding 1165.2.2 Region Growing 1175.2.3 Edge Detection 1175.2.4 Clustering 1175.2.5 Watershed Transform 1175.3 Deep Learning-Based Image Segmentation Methods for Medical Images 1185.3.1 Convolutional Neural Network (CNN) 1195.3.1.1 Convolutional Layer 1195.3.1.2 Activation Function 1205.3.1.3 Pooling Layers 1205.3.1.4 Fully Connected Layers 1205.3.1.5 Softmax Activation 1215.3.1.6 Loss Function and Optimization 1215.3.1.7 Backpropagation 1235.3.2 U-Net 1235.3.3 GoogleNet or Inception 1245.4 Deep Learning Algorithms Employed in the Segmentation of Brain Tumor Images 1255.5 Deep Learning Models for Retinal Vessel Segmentation 1265.6 Deep Learning Models for Inner Ear Segmentation 1285.7 Conclusion 129References 1306 Deep Learning-Based Image Segmentation for Early Detection of Diabetic Retinopathy and Other Retinal Disorders 133Ankur Biswas and Rita Banik6.1 Introduction 1336.2 Deep Learning and Image Segmentation 1356.2.1 Convolutional Neural Networks Architecture 1366.2.2 Pretrained Models and Transfer Learning 1366.2.3 Other Deep Learning Techniques 1376.3 Applications and Benefits of Deep Learning-Based Image Segmentation 1386.3.1 Detection of Diabetic Retinopathy 1386.3.2 Determination of Additional Retinal Conditions 1396.3.2.1 Optic Disc 1396.3.2.2 Microaneurysm 1396.3.2.3 Hemorrhage 1406.3.2.4 Hard Exudates 1406.3.2.5 Soft Exudates (Cotton-Wool Spots) 1406.3.2.6 Retinal Vessel 1416.3.3 Monitoring the Progression of the Disease and Quantitative Analysis 1416.3.4 Healthcare Professionals’ Assistive Tool 1426.4 Challenges and Limitations 1426.4.1 Dataset Quality and Availability 1446.4.2 Explainability and Interpretability of the Model 1446.4.3 Complexity of Computation and Necessity for Resources 1446.4.4 Ethical Issues 1456.5 Conclusions and Future Directions 145References 1467 Analysis of Deep Learning Models for Lung Field Segmentation 149Tairah Andrabi and Sajid Yousuf Bhat7.1 Introduction 1497.2 Medical Imaging Modalities 1517.3 Overview of Classical Approaches for Lung Segmentation in Chest X-rays 1527.3.1 Rule-Based Methods 1527.3.2 Deformable Methods 1537.3.3 Parametric Methods 1547.3.4 Geometric Deformable Models 1547.3.5 Pixel Classifier-Based Segmentation 1557.3.6 Shallow Learning 1567.4 Deep Learning Approaches 1567.4.1 Lung Field Segmentation in Chest X-rays 1567.4.1.1 CNN-Based Approaches 1577.4.1.2 U-Net-Based Approaches 1587.4.1.3 Dilated Convolution-Based Approaches 1607.4.1.4 Attention-Based Approaches 1617.4.1.5 GAN-Based Approaches 1627.4.1.6 Multistage and Ensemble Approaches 1637.4.2 Overview of Deep Learning Approaches for Lung Segmentation in CT Scans 1647.5 Data Sources and Datasets 1667.5.1 Chest X-Ray Datasets 1697.5.1.1 JSRT (“Japanese Society of Radiological Technology”) 1697.5.1.2 Montgomery County (MC) Dataset 1697.5.1.3 Shenzhen Dataset 1697.5.1.4 Indian Dataset 1707.5.1.5 National Institutes of Health Chest X-Ray Dataset (NIH) 1707.5.1.6 COVID-19 Radiography Database 1717.5.2 CT Scan Datasets 1727.6 Evaluation Metrics 1737.7 Conclusion 176References 1768 Generative Adversarial Networks in the Field of Medical Image Segmentation 185Bisma Sultan, Aasia Rehman, and Lubna Riyaz8.1 Introduction 1858.2 Overview of Image Segmentation Techniques 1878.2.1 Traditional Methods for Segmentation of Images 1878.2.2 Deep Learning Methods for Segmentation of Images 1898.3 Generative Adversarial Networks 1908.3.1 Vanilla GAN 1908.3.1.1 GAN Framework 1918.3.2 GAN Variants 1928.3.2.1 InfoGAN 1928.3.2.2 Dcgan 1938.3.2.3 cGAN 1948.3.2.4 Acgan 1948.3.2.5 Wgan 1948.4 Classification of GAN-Based Image Segmentation Techniques 1958.4.1 Classification on the Basis of Segmentation Area 1958.4.1.1 Brain Segmentation Using GAN 1958.4.1.2 Eye Segmentation Using GAN 1978.4.1.3 Cardiology Segmentation Using GAN 1988.4.1.4 Chest Segmentation Using GAN 1998.4.1.5 Breast Segmentation Using GANs 2008.4.1.6 Spine Segmentation Using GANs 2008.4.1.7 Abdomen Segmentation Using GANs 2028.4.1.8 Pelvic Segmentation Using GANs 2028.4.2 Classification on the Basis of Image Modality 2038.4.2.1 Segmentation of Magnetic Resonance Imaging (MRI) Using GAN 2038.4.2.2 Segmentation of Computed Tomography (CT) Images Using Gan 2038.4.2.3 Segmentation of Other Modalities Using GAN 2058.4.3 Classification on the Basis of GAN Model Employed 2068.4.3.1 Segmentation Using U-Net Based GAN 2078.4.3.2 Segmentation Using Conditional GANs (CGAN, pix2pix GAN, and Acgan) 2078.4.3.3 Segmentation Using CycleGAN 2088.4.3.4 Segmentation Using other GAN Models 2108.5 Conclusion 210References 2129 A Collaborative Cell Image Segmentation Model Based on the Multilevel Improvement of Data 227Ishfaq Sheikh, Manzoor Chachoo, and Aijaz Mir9.1 Introduction 2279.2 Methodology 2299.3 Result and Discussion 2349.4 Conclusion and Future Scope 238Acknowledgments 240References 24010 Challenges and Future Directions for Segmentation of Medical Images Using Deep Learning Models 243Roshan Birjais10.1 Introduction 24310.2 Types of Medical Datasets 24410.2.1 X-Ray 24410.2.2 Computerized Tomography 24610.2.3 Mammography (MG) 24710.2.4 Histopathology 24710.2.5 Magnetic Resonance Imaging (MRI) 24910.2.6 Other Images 25010.3 Challenges Related to the Dataset 25010.3.1 Limited Annotated Dataset 25210.3.1.1 Solution 25310.3.2 Sparse Annotations 25410.3.2.1 Solution 25410.3.3 Class Imbalance in Datasets 25410.3.3.1 Solution 25410.3.4 Intensity Inhomogeneities 25510.3.4.1 Solution 25510.3.5 Complexities in Image Texture 25510.3.5.1 Solution 25610.4 Challenges Concerning the DL Models 25610.4.1 Overfitting 25610.4.1.1 Solution 25710.4.2 Space Complexity of Models 25710.4.2.1 Solution 25710.4.2.2 Solution 25710.4.3 Vanishing and Exploding Gradient 25710.4.3.1 Solution 25810.4.4 Computational Complexity 25810.4.4.1 Solution 25810.5 Conclusion 258References 25911 Advancements in Deep Learning for Medical Image Analysis: A Comprehensive Exploration of Techniques, Applications, and Future Prospects 265Vani Malagar, Navin Mani Upadhyay, and Mekhla Sharma11.1 Introduction 26511.2 Significance of Medical Image Segmentation 26611.2.1 Case Studies 26711.3 Deep Learning Techniques for Medical Image Segmentation 26811.3.1 Building Blocks for Medical Image Segmentation 26811.3.1.1 Activation Functions 26911.3.1.2 Loss Functions 26911.3.1.3 Gradient Descent with Backpropagation 27011.3.2 Common Architectures 27011.3.2.1 U-Net 27011.3.2.2 Components 27011.3.2.3 Convolutional Neural Networks (CNNs) 27111.3.2.4 Fully Convolutional Network (FCN) 27111.3.2.5 SegNet 27111.3.2.6 DeepLabv3+ 27311.3.3 Advanced Techniques 27311.3.3.1 Generative Adversarial Networks (GANs) for Data Augmentation 27311.3.3.2 Transformers for Medical Image Segmentation 27411.3.3.3 Ensemble Learning for Improved Performance 27411.4 Applications of Deep Learning in Medical Image Segmentation 27511.4.1 Diagnostic Applications 27511.4.1.1 Improved Tumor Segmentation and Characterization 27511.4.1.2 Lesion Detection and Analysis 27611.4.1.3 Organ Segmentation 27611.4.1.4 Medical Image Analysis for Disease Progression Monitoring 27711.4.2 Therapeutic Applications 27711.5 Challenges and Future Prospects 27711.5.1 Challenges and Limitations 27711.5.2 Future Trends and Advancements 28011.5.2.1 Self-Supervised Learning 28011.5.2.2 Explainable AI (XAI) Techniques 28011.5.2.3 Federated Learning 28211.5.2.4 Integration with AI for Comprehensive Medical Decision Support Systems 28311.6 Conclusion 283References 284Index 285