Streamlined Photoacoustic Image Processing with Foundation Models: A
  Training-Free Solution
Handi Deng Yucheng Zhou Jiaxuan Xiang Liujie Gu Yan Luo Hai Feng Mingyuan Liu Cheng Ma
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.