Medical image segmentation models adapting to new tasks in a training-free manner through in-context learning is an exciting advancement. Universal segmentation models aim to generalize across the diverse modality of medical images, yet their effectiveness often diminishes when applied to out-of-distribution (OOD) data modalities and tasks, requiring intricate fine-tuning of model for optimal performance. For addressing this challenge, we introduce SegICL, a novel approach leveraging In-Context Learning (ICL) for image segmentation. Unlike existing methods, SegICL has the capability to employ text-guided segmentation and conduct in-context learning with a small set of image-mask pairs, eliminating the need for training the model from scratch or fine-tuning for OOD tasks (including OOD modality and dataset). Extensive experimental validation of SegICL demonstrates a positive correlation between the number of prompt samples and segmentation performance on OOD modalities and tasks. This indicates that SegICL effectively address new segmentation tasks based on contextual information. Additionally, SegICL also exhibits comparable segmentation performance to mainstream models on OOD and in-distribution tasks. Our code will be released soon.

翻译：通过上下文学习以无需训练的方式适应新任务的医学图像分割模型是一项令人振奋的进展。通用分割模型旨在泛化医学图像的多模态特性，但其在应用于分布外数据模态和任务时，效果往往显著下降，需要复杂的模型微调才能达到最优性能。为解决这一挑战，我们提出SegICL——一种利用上下文学习进行图像分割的新方法。与现有方法不同，SegICL能够实现文本引导的分割，并借助少量图像-掩码对进行上下文学习，无需从头训练模型或针对分布外任务（包括分布外模态和数据集）进行微调。对SegICL的广泛实验验证表明，提示样本数量与分布外模态及任务的分割性能呈正相关关系，这表明SegICL能基于上下文信息有效处理新分割任务。此外，在分布外和分布内任务上，SegICL展现出与主流模型相当的分割性能。我们的代码将很快开源。