Foundation models have rapidly evolved and have achieved significant
accomplishments in computer vision tasks. Specifically, the prompt mechanism
conveniently allows users to integrate image prior information into the model,
making it possible to apply models without any training. Therefore, we propose
a method based on foundation models and zero training to solve the tasks of
photoacoustic (PA) image segmentation. We employed the segment anything model
(SAM) by setting simple prompts and integrating the model's outputs with prior
knowledge of the imaged objects to accomplish various tasks, including: (1)
removing the skin signal in three-dimensional PA image rendering; (2) dual
speed-of-sound reconstruction, and (3) segmentation of finger blood vessels.
Through these demonstrations, we have concluded that deep learning can be
directly applied in PA imaging without the requirement for network design and
training. This potentially allows for a hands-on, convenient approach to
achieving efficient and accurate segmentation of PA images. This letter serves
as a comprehensive tutorial, facilitating the mastery of the technique through
the provision of code and sample datasets.

该研究论文提出了一种基于基础模型和零训练的方法来解决光声图像分割的任务，通过设置简单提示和将模型输出与图像对象的先验知识相结合，实现了多种任务，包括三维光声图像去除皮肤信号、双声速重建以及手指血管分割。研究结果表明，深度学习可直接应用于光声成像，无需网络设计和训练，从而实现对光声图像的高效准确分割。该论文提供了代码和样本数据集，为技术的掌握提供了全面的指导。