Medical image segmentation is the process of partitioning a medical image (like an MRI, CT, ultrasound, or microscopy image) into meaningful regions, often delineating anatomical structures or pathologies (tumors, organs, blood vessels, etc.). Accurate segmentation is critical for diagnostics, treatment planning, and monitoring (e.g., measuring tumor volume over time). Challenges include low contrast, noise, varying shapes and sizes of anatomy, and sometimes limited labeled data due to required expert annotation. Techniques range from classical methods (thresholding, region growing, active contours) to modern deep learning (U-Net and its variants have become a standard for biomedical segmentation). Evaluation metrics often include Dice coefficient (similar to IoU) and Hausdorff distance. The importance of segmentation in medicine is huge – for example, delineating a tumor in a scan precisely can impact radiation therapy dosing, and segmenting organs helps in surgical planning or understanding disease spread.