  The segmentation foundation model, e.g., Segment Anything Model (SAM), has
attracted increasing interest in the medical image community. Early pioneering
studies primarily concentrated on assessing and improving SAM's performance
from the perspectives of overall accuracy and efficiency, yet little attention
was given to the fairness considerations. This oversight raises questions about
the potential for performance biases that could mirror those found in
task-specific deep learning models like nnU-Net. In this paper, we explored the
fairness dilemma concerning large segmentation foundation models. We
prospectively curate a benchmark dataset of 3D MRI and CT scans of the organs
including liver, kidney, spleen, lung and aorta from a total of 1056 healthy
subjects with expert segmentations. Crucially, we document demographic details
such as gender, age, and body mass index (BMI) for each subject to facilitate a
nuanced fairness analysis. We test state-of-the-art foundation models for
medical image segmentation, including the original SAM, medical SAM and SAT
models, to evaluate segmentation efficacy across different demographic groups
and identify disparities. Our comprehensive analysis, which accounts for
various confounding factors, reveals significant fairness concerns within these
foundational models. Moreover, our findings highlight not only disparities in
overall segmentation metrics, such as the Dice Similarity Coefficient but also
significant variations in the spatial distribution of segmentation errors,
offering empirical evidence of the nuanced challenges in ensuring fairness in
medical image segmentation.
