A narrative review of foundation models for medical image segmentation: zero-shot performance evaluation on diverse modalities.

BACKGROUND AND OBJECTIVE

Foundation models are deep learning models pretrained on extensive datasets, equipped with the ability to adapt to a variety of downstream tasks. Recently, they have gained prominence across various domains, including medical imaging. These models exhibit remarkable contextual understanding and generalization capabilities, spurring active research in healthcare to develop versatile artificial intelligence solutions for real-world clinical environments. Inspired by this, this study offers a comprehensive review of foundation models in medical image segmentation (MIS), evaluates their zero-shot performance on diverse datasets, and assesses their practical applicability in clinical settings.

METHODS

A total of 63 studies on foundation models for MIS were systematically reviewed, utilizing platforms such as arXiv, ResearchGate, Google Scholar, Semantic Scholar, and PubMed. Additionally, we curated 31 unseen medical image datasets from The Cancer Imaging Archive (TCIA), Kaggle, Zenodo, Institute of Electrical and Electronics Engineers (IEEE) DataPort, and Grand Challenge to evaluate the zero-shot performance of six foundation models. Performance analysis was conducted from various perspectives, including modality and anatomical structure.

KEY CONTENT AND FINDINGS

Foundation models were categorized based on a taxonomy that incorporates criteria such as data dimensions, modality coverage, prompt type, and training strategy. Furthermore, the zero-shot evaluation revealed key insights into their strengths and limitations across diverse imaging modalities. This analysis underscores the potential of these models in MIS while highlighting areas for improvement to optimize real-world applications.

CONCLUSIONS

Our findings provide a valuable resource for understanding the role of foundation models in MIS. By identifying their capabilities and limitations, this review lays the groundwork for advancing their practical deployment in clinical environments, supporting further innovation in medical image analysis.