Medical image segmentation is a fundamental process in biomedical analysis that involves partitioning medical images into meaningful regions to identify and delineate anatomical structures, tissues, or pathological areas. This technique enables precise quantification and visualization of organs, lesions, and other clinically relevant features, forming the basis for diagnosis, treatment planning, and disease monitoring. Traditional segmentation methods relied on thresholding, edge detection, and region-growing techniques, but recent advances in deep learning—particularly convolutional neural networks (CNNs) and transformer-based architectures—have revolutionized the field by achieving higher accuracy and robustness across diverse imaging modalities such as MRI, CT, and ultrasound. Automated segmentation not only accelerates clinical workflows but also reduces observer variability, paving the way for personalized and data-driven medicine through improved anatomical understanding and more consistent quantitative assessments.