Flowcell Contrast-Target Autofocus for Stable Blood Cell Counting
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Solution Overview
Problem
Existing automated blood cell analysis systems face challenges in accurately focusing on blood samples due to temperature fluctuations and other environmental factors, leading to out-of-focus images and erroneous cell counting.
Innovation Solution
Implementing autofocus systems that focus on a fixed target within the flowcell, adjusting the distance between the imaging device and the flowcell using a motor drive, without the need for focusing liquids, to maintain optimal focus on the sample stream.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual focusing methods are used, then the system structure remains simple, but temperature fluctuations cause out-of-focus images and erroneous cell counting
Solution Approach 1:
The system performs preliminary autofocus calibration by capturing images at multiple focal positions and determining the optimal focus position before actual blood cell analysis. This preliminary action stores focus position information that compensates for temperature fluctuations during subsequent operations, maintaining reliable focus without continuous adjustment mechanisms.
Solution Approach 2:
The system implements feedback by using captured blood cell images to automatically adjust and determine the optimal focus position. The processor analyzes image quality metrics and adjusts the focal position accordingly, creating a closed-loop system that maintains accurate focus despite temperature changes without requiring complex mechanical focusing mechanisms.
2Measurement precision
If autofocus systems with motor drives are implemented, then focus accuracy is maintained under temperature changes, but the device complexity increases
Solution Approach 1:
The system replaces complex continuous mechanical focusing mechanisms with a digital/image-processing-based autofocus approach. The processor determines optimal focus position by analyzing captured images and calculating the best focal position, substituting mechanical complexity with computational algorithms that achieve high measurement precision for cell counting.
Solution Approach 2:
The system captures multiple images at different focal positions and uses these copies to determine the optimal focus position. By analyzing multiple image copies and comparing focus quality, the system identifies the best focal position without requiring complex real-time mechanical adjustment mechanisms, achieving accurate cell counting through image comparison.
3Stability of the object's composition
If focusing liquids are used to maintain focus, then focus stability is improved, but the system requires additional reagents and increases operational complexity
Solution Approach 1:
The system extracts and eliminates the requirement for focusing liquids or reagents by implementing a digital autofocus mechanism. The processor determines optimal focus position through image analysis and computational methods, removing the need for additional chemical substances while maintaining focus stability, thereby simplifying operation and reducing reagent requirements.
Solution Approach 2:
The system performs self-service autofocus by automatically capturing images, analyzing focus quality, and adjusting focal position without user intervention or external focusing agents. The processor autonomously determines the optimal focus position and maintains stable focus throughout operation, eliminating the need for manual focusing adjustments or focusing liquids.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Ensures high-quality, focused images of blood cells, enabling accurate classification and counting, even in the presence of temperature changes, by using geometric hydrofocusing techniques.
Implementation Method 1
An objective lens associated with a high optical resolution imaging device is disposed on an optical axis that intersects the ribbon-shaped sample stream
Implementation Method 2
The relative distance between the objective and the flowcell is variable by operation of a motor drive
Implementation Method 3
The processor determines a focus position of the autofocus pattern to generate a focused image
Data Source
AI summary
Particles such as blood cells can be categorized and counted by a digital image processor. A digital microscope camera can be directed into a flowcell defining a symmetrically narrowing flowpath in which the sample stream flows in a ribbon flattened by flow and viscosity parameters between layers of sheath fluid. A contrast pattern for autofocusing is provided on the flowcell, for example at an edge of a rear illumination opening. The image processor assesses focus accuracy from pixel data contrast. A positioning motor moves the microscope and/or flowcell along the optical axis for autofocusing on the contrast pattern target. The processor then displaces microscope and flowcell by a known distance between the contrast pattern and the sample stream, thus focusing on the sample stream. Blood cell images are collected from that position until autofocus is reinitiated, periodically, by input signal, or when detecting temperature changes or focus inaccuracy in the image data.