Automated segmentation is a fundamental medical image analysis task, which enjoys significant advances due to the advent of deep learning. While foundation models have been useful in natural language processing and some vision tasks for some time, the foundation model developed with image segmentation in mind—Segment Anything Model (SAM)—has been developed only recently and has shown similar promise. However, there are still no sys- tematic analyses or “best-practice” guidelines for optimal fine-tuning of SAM for medical image segmentation. This work summarizes existing fine-tuning strategies with various backbone architectures, model components, and finetuning algorithms across 18 combinations and evaluates them on 17 datasets covering all common radiology modalities. Our study reveals that (1) fine-tuning SAM leads to slightly better performance than previous segmentation methods, (2) fine-tuning strategies that use parameterefficient learning in both the encoder and decoder are superior to other strategies, (3) network architecture has a small impact on final performance, and (4) further training SAM with self-supervised learning can improve final model performance. We also demonstrate the ineffectiveness of some methods popular in the literature and further expand our experiments into few-shot and prompt-based settings. Lastly, we released our code and MRI-specific fine-tuned weights, which consistently obtained superior performance over the original SAM, at https://github.com/mazurowskilab/finetune-SAM
foundation model · medical image segmentation · Model finetuning · Segment Anything Model
@article{melba:2025:006:gu,
title = "How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model",
author = "Gu, Hanxue and Dong, Haoyu and Yang, Jichen and Mazurowski, Maciej A.",
journal = "Machine Learning for Biomedical Imaging",
volume = "3",
issue = "May 2025 issue",
year = "2025",
pages = "88--120",
issn = "2766-905X",
doi = "https://doi.org/10.59275/j.melba.2025-86a6",
url = "https://melba-journal.org/2025:006"
}
TY - JOUR
AU - Gu, Hanxue
AU - Dong, Haoyu
AU - Yang, Jichen
AU - Mazurowski, Maciej A.
PY - 2025
TI - How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model
T2 - Machine Learning for Biomedical Imaging
VL - 3
IS - May 2025 issue
SP - 88
EP - 120
SN - 2766-905X
DO - https://doi.org/10.59275/j.melba.2025-86a6
UR - https://melba-journal.org/2025:006
ER -