Artificial Intelligence Techniques for Medical Image Segmentation: A Technical Overview and Introduction to Advanced Applications

Medical image segmentation plays a crucial role in enhancing diagnostic, treatment, and research applications within the medical field, increasingly relying on sophisticated artificial intelligence (AI) technologies. This survey explores various AI architectures employed in medical image segmentation, including Deep Neural Networks, Encoder-Decoder Networks, Attention-Based Networks, and hybrid models that integrate features from multiple architectures. It methodically assesses these models’ performances, focusing on their effectiveness across different medical imaging types. The analysis delves into each technique’s specific advantages and limitations, offering valuable insights into their practical applications in medical diagnostics and treatment planning. The comparative study highlights the differences in accuracy, processing time, and the ability to manage complex image structures among the methods reviewed. It also explores the synergistic benefits of hybrid models, which are shown to achieve superior segmentation results. By providing a comprehensive comparison of AI-driven segmentation techniques, the paper significantly enhances our understanding of how these technologies can be optimized and implemented effectively in medical image analysis. The ultimate goal is to guide future research and the practical application of AI in medical image segmentation, aiming to develop more efficient and precise diagnostic tools.