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.

通过 In-Context Learning (ICL) 实现的 SegICL 是一种新颖的方法，它能够在无需训练或微调的情况下，利用文本引导分割和上下文学习来应对在分布之外的数据模态和任务上的新分割任务。实验结果表明，SegICL 能够有效地利用上下文信息来处理新的分割任务，并在在分布和分布之外的任务上展示出可比较的分割性能。