Microdispersion technology is crucial for a variety of applications in both the chemical and biomedical fields. The precise and rapid characterization of microdroplets and microbubbles is essential for research as well as for optimizing and controlling industrial processes. Traditional methods often rely on time-consuming manual analysis. Although some deep learning-based computer vision methods have been proposed for automated identification and characterization

these approaches often rely on supervised learning

which requires labeled data for model training. This dependency on labeled data can be time-consuming and expensive

especially when working with large and complex datasets. To address these challenges

we propose MicroFlowSAM

an innovative

motion-prompted

annotation-free

and training-free instance segmentation approach. By utilizing motion of microdroplets and microbubbles as prompts

our method directs large-scale vision models to perform accurate instance segmentation without the need for annotated data or model training. This approach eliminates the need for human intervention in data labeling and reduces computational costs

significantly streamlining the data analysis process. We demonstrate the effectiveness of MicroFlowSAM across 12 diverse datasets

achieving outstanding segmentation results that are competitive with traditional methods. This novel approach not only accelerates the analysis process but also establishes a foundation for efficient process control and optimization in microfluidic applications. MicroFlowSAM represents a breakthrough in reducing the complexities and resource demands of instance segmentation

enabling faster insights and advancements in the microdispersion field.