Deep Learning Applications in Medical Image Segmentation: Overview, Approaches, and Challenges

fmatter
	Title Page
	Copyright
	Contents
	Acknowledgments
	List of Contributors
	Preface
	Introduction
ch1
	1.1 Introduction
		1.1.1 X‐Rays
		1.1.2 Computed Tomography (CT)
		1.1.3 Medical Resonance Imaging (MRI)
		1.1.4 Positron Emission Tomography (PET)
		1.1.5 Ultrasound (US) Images
		1.1.6 Colonoscopy
		1.1.7 Dermoscopy
		1.1.8 Microscopic Images
		1.1.9 Optical Coherence Tomography (OCT)
	1.2 Datasets for Segmentation of Medical Images
		1.2.1 Multimodal Brain Tumor Segmentation Challenge (BraTS) Dataset
		1.2.2 LIDC‐IDRI (Lung Image Database Consortium Image Collection) Dataset
		1.2.3 LiTS (Liver Tumor Segmentation) Dataset
		1.2.4 KiTS (Kidney Tumor Segmentation) Dataset
		1.2.5 ISIC (International Skin Imaging Collaboration) Dataset
		1.2.6 BUSI (Breast Ultrasound) Dataset
		1.2.7 Colonoscopy Datasets
			1.2.7.1 Kvasir‐SEG
			1.2.7.2 CVC‐ClinicDB and CVC‐ColonDB
	1.3 Augmentation Techniques Used in Medical Image Segmentation
	1.4 Performance Metrics for Evaluating Segmentation Models
		1.4.1 Dice Similarity Coefficient (DSC)
		1.4.2 Intersection over Union (IoU)
		1.4.3 Precision
		1.4.4 Recall
		1.4.5 F1 Score
		1.4.6 Accuracy
	1.5 Conclusion
	References
ch2
	2.1 Introduction
	2.2 Deep Learning Models for Medical Image Segmentation
		2.2.1 Convolutional Neural Network (CNN)
		2.2.2 Fully Convolutional Neural Network (FCN)
		2.2.3 UNet
		2.2.4 Multi‐scale‐Based Models
		2.2.5 Pyramid‐Based Models
		2.2.6 Recurrent Neural Networks (RNNs)
		2.2.7 Attention‐Based Models
		2.2.8 Ensemble‐Based Models
		2.2.9 Other Models
	2.3 Applications of Medical Image Segmentation Models
		2.3.1 Segmentation of Anatomical Organs
			2.3.1.1 Eye
			2.3.1.2 Brain
			2.3.1.3 Liver
			2.3.1.4 Lung
			2.3.1.5 Kidney
			2.3.1.6 Heart
			2.3.1.7 Multi‐organ
	2.4 Current Challenges in Segmentation of Medical Images
	2.5 Conclusion
	References
ch3
	3.1 Introduction
		3.1.1 Contextualizing Medical Image Segmentation
		3.1.2 The Significance of Accurate Segmentation
		3.1.3 Traditional Approaches in Medical Image Segmentation
		3.1.4 Evolution Over Time
		3.1.5 Aims of the Research
	3.2 Literature Review
		3.2.1 Historical Evolution of Medical Image Segmentation
		3.2.2 Thresholding Techniques and Their Applications
		3.2.3 Region‐Based Techniques
		3.2.4 Contour‐Based Algorithms and Their Applications
		3.2.5 Limitations of Traditional Approaches
		3.2.6 The Rise of Deep Learning in Medical Image Segmentation
		3.2.7 Transfer Learning and Multi‐modal Information
		3.2.8 Real‐Time Segmentation and Clinical Applications
		3.2.9 Challenges and Opportunities in the Modern Era
		3.2.10 Future Directions and Research Implications
	3.3 Methodology
		3.3.1 Explanation of Traditional Approaches
			3.3.1.1 Thresholding
			3.3.1.2 Region‐Based Techniques
			3.3.1.3 Contour‐Based Algorithms
		3.3.2 Application in Medical Image Segmentation
			3.3.2.1 Thresholding Applications
			3.3.2.2 Region‐Based Techniques in Practice
			3.3.2.3 Contour‐Based Algorithm Applications
		3.3.3 Datasets and Tools
		3.3.4 Integration of Text Reports and Medical Image Data
	3.4 Historical Context
		3.4.1 Early Heuristic Approaches
		3.4.2 Emergence of Thresholding
		3.4.3 Rise of Region‐Based Techniques
		3.4.4 Introduction of Contour‐Based Algorithms
		3.4.5 Computational Advancements and Modern Era
	3.5 Segmentation
	3.6 Challenges and Opportunities
		3.6.1 Challenges in Traditional Approaches
		3.6.2 Limitations in Handling Modern Imaging Modalities
		3.6.3 Bridging the Gap: Integrating Computational Techniques
		3.6.4 Leveraging Big Data and Real‐World Applications
	3.7 Case Studies
		3.7.1 Application of Traditional Approaches in Clinical Settings
		3.7.2 Challenges Encountered in Real‐World Scenarios
		3.7.3 Integration of Computational Techniques
		3.7.4 Leveraging Big Data for Improved Segmentation
	3.8 Modern Era and Contemporary Techniques
		3.8.1 Evolution Beyond Traditional Approaches
		3.8.2 Role of Deep Learning in Medical Image Segmentation
		3.8.3 Transfer Learning and Generalization
		3.8.4 Integration of Multi‐modal Information
		3.8.5 Real‐Time Segmentation and Clinical Applications
		3.8.6 Challenges and Ongoing Research
	3.9 Conclusion
	References
ch4
	4.1 Introduction
	4.2 Literature Review
		4.2.1 Segmentation of MWM in the Pediatric Brain
		4.2.2 Qualitative and Quantitative Study of Myelination
	4.3 Methodology
		4.3.1 Input Datasets
		4.3.2 Pediatric Brain Extraction and Myelin Segmentation
		4.3.3 Myelin Visualization, Computation of Myelin Index, and Growth Model Fitting
		4.3.4 Study of Hemispheric Differences in Myelination
		4.3.5 Study of Myelination in Premature Babies
	4.4 Results
		4.4.1 Brain Extraction, Myelin Segmentation, and 3D Visualization
		4.4.2 Growth Model Fitting
		4.4.3 Myelination in Right and Left Hemispheres
		4.4.4 Myelination in Premature Babies
	4.5 Discussion
		4.5.1 Clinical Significance of the Study of Myelination
		4.5.2 Modeling Myelination in Neonates, Infants, and Children
		4.5.3 Hemispheric Differences in Myelination
		4.5.4 Preterm Myelination
	4.6 Conclusion
	References
ch5
	5.1 Introduction
	5.2 Classical Image Segmentation Techniques
		5.2.1 Thresholding
		5.2.2 Region Growing
		5.2.3 Edge Detection
		5.2.4 Clustering
		5.2.5 Watershed Transform
	5.3 Deep Learning‐Based Image Segmentation Methods for Medical Images
		5.3.1 Convolutional Neural Network (CNN)
			5.3.1.1 Convolutional Layer
			5.3.1.2 Activation Function
			5.3.1.3 Pooling Layers
			5.3.1.4 Fully Connected Layers
			5.3.1.5 Softmax Activation
			5.3.1.6 Loss Function and Optimization
			5.3.1.7 Backpropagation
		5.3.2 U‐Net
		5.3.3 GoogleNet or Inception
	5.4 Deep Learning Algorithms Employed in the Segmentation of Brain Tumor Images
	5.5 Deep Learning Models for Retinal Vessel Segmentation
	5.6 Deep Learning Models for Inner Ear Segmentation
	5.7 Conclusion
	References
ch6
	6.1 Introduction
	6.2 Deep Learning and Image Segmentation
		6.2.1 Convolutional Neural Networks Architecture
		6.2.2 Pretrained Models and Transfer Learning
		6.2.3 Other Deep Learning Techniques
	6.3 Applications and Benefits of Deep Learning‐Based Image Segmentation
		6.3.1 Detection of Diabetic Retinopathy
		6.3.2 Determination of Additional Retinal Conditions
			6.3.2.1 Optic Disc
			6.3.2.2 Microaneurysm
			6.3.2.3 Hemorrhage
			6.3.2.4 Hard Exudates
			6.3.2.5 Soft Exudates (Cotton‐Wool Spots)
			6.3.2.6 Retinal Vessel
		6.3.3 Monitoring the Progression of the Disease and Quantitative Analysis
		6.3.4 Healthcare Professionals\' Assistive Tool
	6.4 Challenges and Limitations
		6.4.1 Dataset Quality and Availability
		6.4.2 Explainability and Interpretability of the Model
		6.4.3 Complexity of Computation and Necessity for Resources
		6.4.4 Ethical Issues
	6.5 Conclusions and Future Directions
	References
ch7
	7.1 Introduction
	7.2 Medical Imaging Modalities
	7.3 Overview of Classical Approaches for Lung Segmentation in Chest X‐rays
		7.3.1 Rule‐Based Methods
		7.3.2 Deformable Methods
		7.3.3 Parametric Methods
		7.3.4 Geometric Deformable Models
		7.3.5 Pixel Classifier‐Based Segmentation
		7.3.6 Shallow Learning
	7.4 Deep Learning Approaches
		7.4.1 Lung Field Segmentation in Chest X‐rays
			7.4.1.1 CNN‐Based Approaches
			7.4.1.2 U‐Net‐Based Approaches
			7.4.1.3 Dilated Convolution‐Based Approaches
			7.4.1.4 Attention‐Based Approaches
			7.4.1.5 GAN‐Based Approaches
			7.4.1.6 Multistage and Ensemble Approaches
		7.4.2 Overview of Deep Learning Approaches for Lung Segmentation in CT Scans
	7.5 Data Sources and Datasets
		7.5.1 Chest X‐Ray Datasets
			7.5.1.1 JSRT (“Japanese Society of Radiological Technology”)
			7.5.1.2 Montgomery County (MC) Dataset
			7.5.1.3 Shenzhen Dataset
			7.5.1.4 Indian Dataset
			7.5.1.5 National Institutes of Health Chest X‐Ray Dataset (NIH)
			7.5.1.6 COVID‐19 Radiography Database
		7.5.2 CT Scan Datasets
	7.6 Evaluation Metrics
	7.7 Conclusion
	References
ch8
	8.1 Introduction
	8.2 Overview of Image Segmentation Techniques
		8.2.1 Traditional Methods for Segmentation of Images
		8.2.2 Deep Learning Methods for Segmentation of Images
	8.3 Generative Adversarial Networks
		8.3.1 Vanilla GAN
			8.3.1.1 GAN Framework
		8.3.2 GAN Variants
			8.3.2.1 InfoGAN
			8.3.2.2 DCGAN
			8.3.2.3 cGAN
			8.3.2.4 ACGAN
			8.3.2.5 WGAN
	8.4 Classification of GAN‐Based Image Segmentation Techniques
		8.4.1 Classification on the Basis of Segmentation Area
			8.4.1.1 Brain Segmentation Using GAN
			8.4.1.2 Eye Segmentation Using GAN
			8.4.1.3 Cardiology Segmentation Using GAN
			8.4.1.4 Chest Segmentation Using GAN
			8.4.1.5 Breast Segmentation Using GANs
			8.4.1.6 Spine Segmentation Using GANs
			8.4.1.7 Abdomen Segmentation Using GANs
			8.4.1.8 Pelvic Segmentation Using GANs
		8.4.2 Classification on the Basis of Image Modality
			8.4.2.1 Segmentation of Magnetic Resonance Imaging (MRI) Using GAN
			8.4.2.2 Segmentation of Computed Tomography (CT) Images Using GAN
			8.4.2.3 Segmentation of Other Modalities Using GAN
		8.4.3 Classification on the Basis of GAN Model Employed
			8.4.3.1 Segmentation Using U‐Net Based GAN
			8.4.3.2 Segmentation Using Conditional GANs (CGAN, pix2pix GAN, and ACGAN)
			8.4.3.3 Segmentation Using CycleGAN
			8.4.3.4 Segmentation Using other GAN Models
	8.5 Conclusion
	References
ch9
	9.1 Introduction
	9.2 Methodology
	9.3 Result and Discussion
	9.4 Conclusion and Future Scope
	Acknowledgments
	References
ch10
	10.1 Introduction
	10.2 Types of Medical Datasets
		10.2.1 X‐Ray
		10.2.2 Computerized Tomography
		10.2.3 Mammography (MG)
		10.2.4 Histopathology
		10.2.5 Magnetic Resonance Imaging (MRI)
		10.2.6 Other Images
	10.3 Challenges Related to the Dataset
		10.3.1 Limited Annotated Dataset
			10.3.1.1 Solution
		10.3.2 Sparse Annotations
			10.3.2.1 Solution
		10.3.3 Class Imbalance in Datasets
			10.3.3.1 Solution
		10.3.4 Intensity Inhomogeneities
			10.3.4.1 Solution
		10.3.5 Complexities in Image Texture
			10.3.5.1 Solution
	10.4 Challenges Concerning the DL Models
		10.4.1 Overfitting
			10.4.1.1 Solution
		10.4.2 Space Complexity of Models
			10.4.2.1 Solution
			10.4.2.2 Solution
		10.4.3 Vanishing and Exploding Gradient
			10.4.3.1 Solution
		10.4.4 Computational Complexity
			10.4.4.1 Solution
	10.5 Conclusion
	References
ch11
	11.1 Introduction
	11.2 Significance of Medical Image Segmentation
		11.2.1 Case Studies
	11.3 Deep Learning Techniques for Medical Image Segmentation
		11.3.1 Building Blocks for Medical Image Segmentation
			11.3.1.1 Activation Functions
			11.3.1.2 Loss Functions
			11.3.1.3 Gradient Descent with Backpropagation
		11.3.2 Common Architectures
			11.3.2.1 U‐Net
			11.3.2.2 Components
			11.3.2.3 Convolutional Neural Networks (CNNs)
			11.3.2.4 Fully Convolutional Network (FCN)
			11.3.2.5 SegNet
			11.3.2.6 DeepLabv3+
		11.3.3 Advanced Techniques
			11.3.3.1 Generative Adversarial Networks (GANs) for Data Augmentation
			11.3.3.2 Transformers for Medical Image Segmentation
			11.3.3.3 Ensemble Learning for Improved Performance
	11.4 Applications of Deep Learning in Medical Image Segmentation
		11.4.1 Diagnostic Applications
			11.4.1.1 Improved Tumor Segmentation and Characterization
			11.4.1.2 Lesion Detection and Analysis
			11.4.1.3 Organ Segmentation
			11.4.1.4 Medical Image Analysis for Disease Progression Monitoring
		11.4.2 Therapeutic Applications
	11.5 Challenges and Future Prospects
		11.5.1 Challenges and Limitations
		11.5.2 Future Trends and Advancements
			11.5.2.1 Self‐Supervised Learning
			11.5.2.2 Explainable AI (XAI) Techniques
			11.5.2.3 Federated Learning
			11.5.2.4 Integration with AI for Comprehensive Medical Decision Support Systems
	11.6 Conclusion
	References
index