A Barcode-Array Biochip Sensor Analysis System Based on Geometry-Guide Learning

In recent years, the biochip sensor has gained significant attention for its extensive utilization in drug screening, biochemical pathway profiling, and genetic sequencing. The barcode-array biochips have emerged as a promising biochip sensor technology due to their high throughput and rapid detection capabilities. However, to achieve optimal analysis results, it is crucial to effectively integrate each step of the biochip sensor analysis. Unfortunately, commercial scanner systems suffer from manual redundancy processing and low accuracy issues. To that end, this article presents an automatic barcode-array biochip sensor analysis system that enhances efficiency and accuracy. The proposed system introduces a novel barcode-array biochip sensor design that facilitates data analysis. Additionally, a confocal laser scanner with a lightweight scanning strategy is employed to improve scanning efficiency. Furthermore, a geometry-guide learning (GGL) method ensures accurate barcode segmentation, which improves the cooperation between barcode-array biochip sensor analysis and biochip fabrication. The GGL approach incorporates prior region prompts and specific scanning strategies, resulting in an F1-score of 86.17 for barcode segmentation, overcoming the limitations faced by existing segmentation algorithms when confronted with extreme challenges. Moreover, the lightweight scanning strategy improves data acquisition efficiency by 53.2% while saving 67.5% disk space usage. The linear regression ${R^{{2}}}$ value of fluorescence emission intensity exceeds 0.99, indicating that the proposed system is suitable for both research purposes and clinical diagnostics applications. Notably, this article represents the first integrated solution for barcode-array biochip sensors that effectively addresses the challenges encountered at each step.