Large foundation models, known for their strong zero-shot generalization, have excelled in visual and language applications. However, applying them to medical image segmentation, a domain with diverse imaging types and target labels, remains an open challenge. Current approaches, such as adapting interactive segmentation models like Segment Anything Model (SAM), require user prompts for each sample during inference. Alternatively, transfer learning methods like few/one-shot models demand labeled samples, leading to high costs. This paper introduces a new paradigm toward the universal medical image segmentation, termed 'One-Prompt Segmentation.' One-Prompt Segmentation combines the strengths of one-shot and interactive methods. In the inference stage, with just \textbf{one prompted sample}, it can adeptly handle the unseen task in a single forward pass. We train One-Prompt Model on 64 open-source medical datasets, accompanied by the collection of over 3,000 clinician-labeled prompts. Tested on 14 previously unseen datasets, the One-Prompt Model showcases superior zero-shot segmentation capabilities, outperforming a wide range of related methods. The code and data is released as https://github.com/KidsWithTokens/one-prompt.

翻译：大型基础模型以其强大的零样本泛化能力，在视觉和语言应用中表现出色。然而，将其应用于医学图像分割这一涵盖多种成像类型和目标标签的领域，仍是一项公开挑战。当前方法，如适配交互式分割模型（例如Segment Anything Model，简称SAM），需要在推理阶段为每个样本提供用户提示。此外，像少样本/单样本模型这样的迁移学习方法需要标记样本，导致成本高昂。本文提出了一种面向通用医学图像分割的新范式，称为“单提示分割”。单提示分割结合了单样本方法和交互式方法的优势。在推理阶段，仅需**一个提示样本**，即可在一次前向传播中灵活处理未见过的任务。我们在64个开源医学数据集上训练了单提示模型，并收集了超过3000个临床医生标注的提示。在14个先前未见过的数据集上测试，单提示模型展示了卓越的零样本分割能力，优于广泛的相关方法。代码和数据已发布在https://github.com/KidsWithTokens/one-prompt。