Core Concepts
Foundation models can be directly applied to photoacoustic image processing tasks without the need for network design and training, enabling efficient and accurate segmentation through the integration of prior knowledge.
Abstract
This paper presents a method called SAMPA (SAM-assisted PA image processing) that leverages the Segment Anything Model (SAM), a foundation model, to perform photoacoustic (PA) image processing in a training-free manner. The key highlights are:
SAMPA utilizes the SAM model to segment PA images by incorporating simple prompts, without requiring any model training.
The segmentation results from SAM are then combined with prior knowledge about the imaged objects to enable various downstream processing tasks, such as:
Removing skin signals in 3D PA imaging of the human hand to better reveal deeper blood vessels.
Facilitating dual speed-of-sound reconstruction in 2D mouse imaging by accurately delineating the animal's boundary.
Refining blood vessel segmentation in human finger imaging by post-processing the initial SAM output.
SAMPA demonstrates strong robustness, achieving good segmentation results even in the presence of artifacts like limited-view and under-sampling.
The method is highly efficient, with the SAM model able to perform segmentation on 500x500 pixel images within 0.1 seconds, making it suitable for practical deployment.
By eliminating the need for dataset preparation and network design, SAMPA provides a convenient, training-free approach to applying deep learning in photoacoustic imaging, paving the way for wider adoption of such techniques.
Stats
The paper does not provide any specific numerical data or metrics to support the key logics. The main focus is on demonstrating the effectiveness of the proposed SAMPA method through qualitative results and comparisons.
Quotes
The paper does not contain any striking quotes that directly support the key logics.