  Most state-of-the-art techniques for medical image segmentation rely on
deep-learning models. These models, however, are often trained on
narrowly-defined tasks in a supervised fashion, which requires expensive
labeled datasets. Recent advances in several machine learning domains, such as
natural language generation have demonstrated the feasibility and utility of
building foundation models that can be customized for various downstream tasks
with little to no labeled data. This likely represents a paradigm shift for
medical imaging, where we expect that foundation models may shape the future of
the field. In this paper, we consider a recently developed foundation model for
medical image segmentation, UniverSeg. We conduct an empirical evaluation study
in the context of prostate imaging and compare it against the conventional
approach of training a task-specific segmentation model. Our results and
discussion highlight several important factors that will likely be important in
the development and adoption of foundation models for medical image
segmentation.
