# Streamlined Photoacoustic Image Processing with Foundation Models: A Training-Free Solution

arXiv: https://arxiv.org/abs/2404.07833 · HTML: https://mlsanity.com/paper/2404.07833/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 3 references that are in the map.
- First posted on arXiv 11 Apr 2024.

## Builds on (most cited first, 1 of 1)

- [Review of Large Vision Models and Visual Prompt Engineering](https://mlsanity.com/paper/2307.00855/) (2023)

## Built on by (most cited first, 1 of 1)

- [Advances in Photoacoustic Imaging Reconstruction and Quantitative Analysis for Biomedical Applications](https://mlsanity.com/paper/2411.02843/) (2024)

## Abstract (arXiv)

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.