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

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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.

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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.