Microfluidic Image Analysis With Adaptive ROI Alignment
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Solution Overview
Problem
Existing microfluidic analysis systems face inaccuracies due to variable ROI shapes, locations, and orientations, as well as the presence of impurities like air bubbles and light scattering particles, leading to inconsistent and inaccurate analyte measurements.
Innovation Solution
A microfluidic analysis system that adaptively identifies ROIs using reference features and clustering-based thresholding, correcting for alignment and impurities to ensure accurate analyte measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fixed ROI methods are used, then the device complexity is low, but the measurement precision deteriorates due to variable ROI shapes, locations, and orientations
Solution Approach 1:
The system dynamically adapts the ROI identification process to account for variable container orientations, shapes, and positions. The method automatically adjusts ROI parameters based on detected container characteristics and fluid properties, transforming a static fixed-ROI approach into a dynamic adaptive system that maintains measurement precision across varying conditions.
Solution Approach 2:
The system changes multiple parameters simultaneously including ROI location, orientation, shape, and size based on detected container and fluid characteristics. By adjusting these parameters adaptively rather than using fixed values, the system resolves the contradiction between maintaining low device complexity and achieving high measurement precision under variable conditions.
2Measurement precision
If impurities like air bubbles and light scattering particles are present, then the ease of operation is maintained, but the measurement precision deteriorates
Solution Approach 1:
The system extracts and removes the effects of impurities from the measurement process. By identifying and excluding regions containing air bubbles and light scattering particles from the ROI analysis, the system isolates the harmful factors and prevents them from degrading measurement precision, while maintaining ease of operation through automated detection and exclusion.
Solution Approach 2:
The system converts the presence of impurities into a detectable signal that triggers automated correction. By using the optical effects of impurities (light scattering, refraction) as detection markers, the system identifies their locations and applies corrective measures, transforming a harmful factor into a useful indicator for quality control and measurement validation.
3Measurement precision
If alignment correction is performed, then the measurement precision is improved, but the processing time increases
Solution Approach 1:
The system performs preliminary alignment correction and ROI identification before the main measurement process. By detecting container orientation, position, and shape upfront and pre-adjusting the ROI parameters accordingly, the system eliminates the need for time-consuming real-time corrections during measurement, thus improving precision without significant time penalty.
Solution Approach 2:
The system replaces manual alignment and ROI selection with automated image processing and computational algorithms. By substituting mechanical adjustment procedures with software-based detection and correction, the system achieves rapid alignment that maintains measurement precision while minimizing processing time through efficient computational methods.
Data Source
AI summary
Technology described herein includes a method that includes obtaining an image of a fluid of a microfluidic analysis system. The microfluidic analysis system includes or receives a container that contains the fluid for measurement of analyte or quality determination. A region of interest (ROI) is identified based on the image. The ROI is a set of pixel values for use in the measurement of the analyte or the quality determination of the fluid, fluidic path, or measuring system. Identifying the ROI includes: determining an alignment of the container of the fluid with the imaging device based on the image, and identifying the ROI based on information about the measurement of the fluid or based on information about non-analyte features of the fluid. An analysis of the image of the fluid is performed using the set of pixel values of the ROI.