MLP Cell Classification via Microfluidic Alignment
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Solution Overview
Problem
Conventional cell classification and sorting methods require time- and cost-intensive labeling, which can alter cellular properties and are not suitable for high-throughput applications, especially in real-time scenarios like transplantation, and lack efficient techniques for label-free, marker-free cell classification and sorting.
Innovation Solution
A classifying device and method using a multilayer perceptron (MLP) for real-time classification of cells based on bright-field images, aligning cells along their major axis within a microfluidic channel, and sorting them using a sorting unit based on classification results without the need for labeling, enabling high-throughput, label-free cell classification and sorting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional fluorescent or magnetic labeling is used for cell classification and sorting, then classification accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The invention extracts and removes the labeling step from the conventional cell classification workflow. By using bright-field imaging to capture intrinsic optical properties of cells directly, the method eliminates the need for fluorescent or magnetic labels, thereby removing the time-consuming labeling process while maintaining classification capability through alternative optical feature extraction
Solution Approach 2:
The invention substitutes the mechanical/chemical labeling process with an optical detection system. Instead of physically attaching dyes or antibodies to cells, the system uses bright-field microscopy to capture light scattering and absorption patterns, replacing a multi-step wet lab procedure with a direct optical measurement that provides classification information
2Measurement precision
If conventional fluorescent labeling is used for cell classification, then cell identification is improved, but cellular properties and function may be altered
Solution Approach 1:
The invention extracts and eliminates the labeling step from the classification workflow. By using bright-field imaging to capture intrinsic optical properties of cells directly, the method removes the need for fluorescent or magnetic labels that could alter cellular properties, thereby maintaining cell viability and natural function while achieving classification
Solution Approach 2:
The invention enables cells to serve themselves for classification by utilizing their intrinsic optical properties without external modification. The bright-field imaging technique captures natural light scattering and absorption characteristics of cells, allowing the cells to provide their own classification information without requiring exogenous labels that could interfere with their biological properties
3Object-affected harmful factors
If label-free cell classification is implemented, then cellular properties are preserved, but throughput and real-time classification capability are reduced
Solution Approach 1:
The invention performs preliminary alignment of cells along their major axis using microfluidic structures before imaging. This pre-processing step standardizes cell orientation and position in the field of view, enabling faster image processing and classification while maintaining label-free operation, thus resolving the throughput limitation
Solution Approach 2:
The invention employs a dynamic multilayer perceptron (MLP) neural network that processes images in real-time as cells flow through the microfluidic device. The system adapts to the continuous flow of cells, performing classification dynamically rather than in static batches, thereby achieving both high throughput and real-time capability without compromising cell properties
4Object-affected harmful factors
If bright-field images are used for cell classification, then labeling is eliminated, but real-time classification and sorting control is not achievable
Solution Approach 1:
The invention performs preliminary alignment of cells along their major axis using microfluidic structures before imaging. This pre-processing step standardizes cell orientation and position, reducing the computational complexity of image analysis and enabling faster real-time classification that can keep up with high-throughput cell flow rates
Solution Approach 2:
The invention substitutes complex image processing algorithms with a trained multilayer perceptron neural network that performs classification in a single pass. This computational substitution replaces multi-step image analysis with a streamlined neural network inference process that operates at the speeds required for real-time sorting control
Data Source
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AI summary
A classifying device for classifying cells in real-time, comprising: an alignment unit configured to align a cell to be classified along the cell's major axis; and a classifying unit configured to classify the aligned cell using a multilayer perceptron, MLP; wherein the MLP classifies the aligned cell based on one or more images of the aligned cell. By executing the classifying device, an improved and efficient cell classification in real-time based on cell images can be provided, while labelling of the cells to be classified can be avoided.