Deep Learning Applications in Medical Image Segmentation

Overview, Approaches, and Challenges

Sajid Yousuf Bhat, Aasia Rehman, Muhammad Abulaish

• Publisher: Wiley-IEEE Press
• Year: 2025
• Print ISBN: 9781394245338
• eBook ISBN: 9781394245352
• Edition: 1
• Topic: Computer Science
• Subtopic: Digital Media
• Index: Computer Science

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.

Table of contents

• Cover
• Title Page
• Copyright
• Contents
• Acknowledgments
• List of Contributors
• Preface
• Introduction
• Chapter 1 Introduction to Medical Image Segmentation: Overview of Modalities, Benchmark Datasets, Data Augmentation Techniques, and Evaluation Metrics
• Chapter 2 Fundamentals of Deep Learning Models for Medical Image Segmentation
• Chapter 3 Revealing Historical Insights: A Comprehensive Exploration of Traditional Approaches in Medical Image Segmentation
• Chapter 4 Segmentation and Quantitative Analysis of Myelinated White Matter Tissue in Pediatric Brain Magnetic Resonance Images
• Chapter 5 Deep Learning Transformations in Medical Imaging: Advancements in Brain Tumor, Retinal Vessel, and Inner Ear Segmentation
• Chapter 6 Deep Learning‐Based Image Segmentation for Early Detection of Diabetic Retinopathy and Other Retinal Disorders
• Chapter 7 Analysis of Deep Learning Models for Lung Field Segmentation
• Chapter 8 Generative Adversarial Networks in the Field of Medical Image Segmentation
• Chapter 9 A Collaborative Cell Image Segmentation Model Based on the Multilevel Improvement of Data
• Chapter 10 Challenges and Future Directions for Segmentation of Medical Images Using Deep Learning Models
• Chapter 11 Advancements in Deep Learning for Medical Image Analysis: A Comprehensive Exploration of Techniques, Applications, and Future Prospects
• Index
• EULA