Machine Learning In Biomedical Image Segmentation: A Technical Review

The automation of anatomical and pathological region identification in clinical imaging has become a cornerstone of modern diagnostics. This review presents a systematic exploration of machine learning paradigms—from classical statistical models to cutting-edge foundation architectures—and their role in transforming segmentation accuracy, speed, and generalizability. We dissect foundational techniques such as kernel-based classifiers, ensemble tree models, and probabilistic graphical frameworks, contrasting them with deep learning systems including convolutional, recurrent, and transformer-based networks. Performance metrics from 2022–2025 benchmarks are synthesized across MRI, CT, ultrasound, and pathology datasets. We address persistent barriers—annotation scarcity, class imbalance, domain shift, and computational overhead—and evaluate mitigation strategies like transfer learning, synthetic data generation, and prompt-driven inference. A dedicated section introduces 2020–2025 breakthroughs: vision transformers, large-scale pre-trained models (e.g., MedSAM), diffusion-based synthesis, and hybrid neuro-symbolic systems. The convergence of these innovations signals a paradigm shift toward universal, data-efficient, and clinically deployable segmentation.