Computer Vision and Artificial Intelligent Techniques for Medical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Application

Medical image segmentation is a crucial task in computer vision, playing a fundamental role in applications like diagnosis, treatment planning, and medical research. This article offers a comprehensive survey of various methods employed in medical research for image segmentation. These techniques range from traditional approaches based on thresholds, regions, edges, and clustering, to modern artificial intelligence methods, particularly deep learning techniques. The strengths and limitations of each method are meticulously examined. Furthermore, recent advancements in segmentation methods are scrutinized, emphasizing their potential to enhance both accuracy and efficiency. The study presents results from multiple approaches, accompanied by a detailed analysis of the strengths and weaknesses inherent in the diverse techniques applied to medical image segmentation. This paper focuses on analyzing various architectures used for medical image segmentation, specifically evaluating their performance. It aims to deeply explore the different segmentation methods, offering a comparative perspective on their effectiveness. This study contributes to a better understanding of the applicability of these techniques in the medical field, particularly in computer vision.