SAMPLS: A prompt engineering approach using Segment-Anything-Model for PLant Science research

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Virginia Tech


Comparative anatomical studies of diverse plant species are vital for the understanding of changes in gene functions such as those involved in solute transport and hormone signaling in plant roots. The state-of-the-art method for confocal image analysis called PlantSeg utilized U-Net for cell wall segmentation. U-Net is a neural network model that requires training with a large amount of manually labeled confocal images and lacks generalizability. In this research, we test a foundation model called the Segment Anything Model (SAM) to evaluate its zero-shot learning capability and whether prompt engineering can reduce the effort and time consumed in dataset annotation, facilitating a semi-automated training process. Our proposed method improved the detection rate of cells and reduced the error rate as compared to state-of-the-art segmentation tools. We also estimated the IoU scores between the proposed method and PlantSeg to reveal the trade-off between accuracy and detection rate for different quality of data. By addressing the challenges specific to confocal images, our approach offers a robust solution for studying plant structure. Our findings demonstrated the efficiency of SAM in confocal image segmentation, showcasing its adaptability and performance as compared to existing tools. Overall, our research highlights the potential of foundation models like SAM in specialized domains and underscores the importance of tailored approaches for achieving accurate semantic segmentation in confocal imaging.



Segment-Anything-Model, Large-Vision-Models, Vision Transformers, Semantic segmentation, Prompt Segmentation, Interactive machine learning