Segment anything model for medical image analysis: An experimental study

Maciej A Mazurowski, Haoyu Dong, Hanxue Gu, Jichen Yang, Nicholas Konz, Yixin Zhang

Abstract:

Training segmentation models for medical images continues to be challenging due to the limited availability of
data annotations. Segment Anything Model (SAM) is a foundation model trained on over 1 billion annotations,
predominantly for natural images, that is intended to segment user-defined objects of interest in an interactive
manner. While the model performance on natural images is impressive, medical image domains pose their
own set of challenges. Here, we perform an extensive evaluation of SAM’s ability to segment medical images
on a collection of 19 medical imaging datasets from various modalities and anatomies. In our experiments, we
generated point and box prompts for SAM using a standard method that simulates interactive segmentation.
We report the following findings: (1) SAM’s performance based on single prompts highly varies depending
on the dataset and the task, from IoU=0.1135 for spine MRI to IoU=0.8650 for hip X-ray. (2) Segmentation
performance appears to be better for well-circumscribed objects with prompts with less ambiguity such as
the segmentation of organs in computed tomography and poorer in various other scenarios such as the
segmentation of brain tumors. (3) SAM performs notably better with box prompts than with point prompts. (4)
SAM outperforms similar methods RITM, SimpleClick, and FocalClick in almost all single-point prompt settings.
(5) When multiple-point prompts are provided iteratively, SAM’s performance generally improves only slightly
while other methods’ performance improves to the level that surpasses SAM’s point-based performance. We
also provide several illustrations for SAM’s performance on all tested datasets, iterative segmentation, and
SAM’s behavior given prompt ambiguity. We conclude that SAM shows impressive zero-shot segmentation
performance for certain medical imaging datasets, but moderate to poor performance for others. SAM has
the potential to make a significant impact in automated medical image segmentation in medical imaging, but
appropriate care needs to be applied when using it. Code for evaluation SAM is made publicly available at
https://github.com/mazurowski-lab/segment-anything-medical-evaluation