Microfluidic Image Analysis With Adaptive ROI Detection
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Solution Overview
Problem
Existing microfluidic image analysis devices face challenges in accurately identifying regions of interest (ROIs) due to variations in the alignment of containers with imaging devices and the presence of impurities like air bubbles and lipids, which can lead to inaccurate analyte measurements and quality determinations.
Innovation Solution
The technology employs adaptive and automatic systems that identify ROIs in images without predetermined assumptions about shape, location, or orientation. It uses reference features from the container, such as edges, to correct for alignment variations and dynamically determines sample-specific, arbitrary-shaped ROIs, potentially accounting for impurities and particles within the ROIs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fixed-assumption ROI identification methods are used, then the system is simpler to implement, but measurement precision deteriorates due to alignment variations and impurities
Solution Approach 1:
The system dynamically adjusts ROI identification based on actual image content rather than using fixed predetermined assumptions. The method adapts to varying container alignments and sample conditions by detecting features in real-time, allowing the ROI selection process to be flexible and responsive to actual measurement conditions
Solution Approach 2:
The system performs self-correction by automatically detecting and compensating for alignment variations and impurities without external intervention. The ROI identification process autonomously adjusts to account for air bubbles, lipids, and misalignments by analyzing image features and dynamically selecting appropriate regions
2Measurement precision
If adaptive ROI identification is implemented, then measurement precision improves, but processing time increases due to complex image analysis
Solution Approach 1:
The image processing is divided into distinct stages: initial feature detection, candidate ROI identification, impurity detection and exclusion, and final ROI confirmation. This segmentation allows the system to process images systematically, focusing computational resources on critical analysis steps while maintaining overall efficiency
3Measurement precision
If impurities like air bubbles and lipids are accounted for, then measurement precision improves, but device complexity increases due to additional detection requirements
Solution Approach 1:
The system extracts and isolates impurity regions (air bubbles, lipids) from the main sample area by detecting their distinct optical characteristics. Once identified, these impurities are excluded from the ROI, allowing clean measurement of the actual analyte without interference from contaminating elements
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.