Streamlined Photoacoustic Image Processing with Foundation Models: A Training-Free Solution
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929311135563776 |
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| author | Deng, Handi Zhou, Yucheng Xiang, Jiaxuan Gu, Liujie Luo, Yan Feng, Hai Liu, Mingyuan Ma, Cheng |
| author_facet | Deng, Handi Zhou, Yucheng Xiang, Jiaxuan Gu, Liujie Luo, Yan Feng, Hai Liu, Mingyuan Ma, Cheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_07833 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Streamlined Photoacoustic Image Processing with Foundation Models: A Training-Free Solution Deng, Handi Zhou, Yucheng Xiang, Jiaxuan Gu, Liujie Luo, Yan Feng, Hai Liu, Mingyuan Ma, Cheng Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Streamlined Photoacoustic Image Processing with Foundation Models: A Training-Free Solution |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2404.07833 |