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.

翻译：大多数最先进的医学图像分割技术依赖于深度学习模型。然而，这些模型通常以监督学习的方式在定义狭窄的任务上进行训练，这需要昂贵的标注数据集。近年来，自然语言生成等多个机器学习领域的最新进展证明了构建基础模型的可行性和实用性，这些模型可以在几乎不需要标注数据的情况下针对各种下游任务进行定制。这很可能代表了医学影像领域的一种范式转变，我们预计基础模型将塑造该领域的未来。本文针对近期开发的医学图像分割基础模型UniverSeg，在前列腺影像场景下开展实证评估研究，并将其与传统训练任务特定分割模型的方法进行比较。我们的结果和讨论强调了在开发和应用医学图像分割基础模型时可能至关重要的几个重要因素。