AI-Assisted Segmentation in Biomedical Imaging

This publication explores the use of artificial intelligence for high-precision segmentation in biomedical imaging. It reviews convolutional and transformer-based architectures applied to magnetic resonance, computed tomography, and microscopy datasets, with attention to annotation quality, model validation, and domain shift. The article argues that reliable clinical adoption requires interpretable outputs, standardized benchmarks, and prospective evaluation across diverse patient populations.