Tóm tắt

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.rnrnDeep 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.rnrnReaders will also find:rnrnAnalysis of deep learning models, including FCN, UNet, SegNet, Dee Lab, and many morernDetailed discussion of medical image segmentation divided by area, incorporating all major organs and organ systemsrnRecent deep learning advancements in segmenting brain tumors, retinal vessels, and inner ear structuresrnAnalyzes the effectiveness of deep learning models in segmenting lung fields for respiratory disease diagnosisrnExplores the application and benefits of Generative Adversarial Networks (GANs) in enhancing medical image segmentationrnIdentifies and discusses the key challenges faced in medical image segmentation using deep learning techniquesrnProvides an overview of the latest advancements, applications, and future trends in deep learning for medical image analysis