Deep Learning Cell Sorting for High-Accuracy Fluorescence Evaluation
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Solution Overview
Problem
Traditional cell sorting methods, such as threshold segmentation and template matching, face challenges in providing accurate cell sorting due to variations in cell morphologies and fluorescence signals, leading to low accuracy and inefficiency.
Innovation Solution
A cell sorting method utilizing deep learning to locate and evaluate fluorescence signal intensities of cells, combined with image processing techniques for precise cell sorting, and a cell display method involving imaging mechanisms to enhance clarity and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional threshold segmentation method is used for cell sorting, then the process is simple to implement, but the sorting accuracy is low due to inability to provide appropriate thresholds for all backgrounds and cell morphologies
Solution Approach 1:
The patent replaces traditional mechanical/image processing-based cell sorting methods (threshold segmentation, template matching) with a deep learning-based automated system. The deep learning model automatically learns optimal sorting criteria from training data, substituting manual threshold setting and mechanical image processing with intelligent algorithms that adapt to various cell morphologies and backgrounds, thereby improving sorting accuracy while maintaining ease of implementation.
2Ease of operation
If traditional template matching method is used for cell sorting, then the process is straightforward, but the cell matching accuracy is low resulting in poor sorting results
Solution Approach 1:
The patent substitutes traditional template matching operations with deep learning-based image recognition. The deep learning model automatically extracts features and performs matching without requiring manual template creation or adjustment, improving cell matching accuracy while maintaining operational simplicity through automated processing.
Solution Approach 2:
The patent changes the parameters used for cell matching from fixed template parameters to dynamic parameters learned by the deep learning model. The model adapts its matching criteria based on the specific characteristics of each cell type and imaging condition, allowing accurate matching across diverse cell morphologies without requiring manual parameter adjustment for each case.
3Measurement precision
If deep learning model is used for cell locating and fluorescence evaluation, then the sorting accuracy is improved, but the device complexity and computational requirements increase
Solution Approach 1:
The patent implements a universal deep learning model that performs multiple functions: cell locating, fluorescence signal evaluation, and sorting decision-making. This multi-functional model reduces the need for separate specialized systems for each task, thereby improving sorting accuracy while managing overall system complexity through consolidation of functions into a single intelligent platform.
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
The method achieves high-accuracy, high-throughput cell sorting with reduced human interference, ensuring morphologically intact and viable single cells, and improves sorting efficiency by leveraging deep learning and advanced imaging.
Implementation Method 1
imaging the cells to be sorted in the preset region by the imaging mechanism to acquire cell image data
Implementation Method 2
irradiating, by the fluorescence assembly, the cells to be sorted to cause the cells to be sorted to emit fluorescence
Data Source
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AI summary
The present application relates to a cell sorting method and a cell display method. The cell sorting method includes: acquiring cell image data sent from a cell sorting device (202); locating cells to be sorted in the cell image data through a deep learning model (204); evaluating fluorescence signal intensities of the cells to be sorted based on the cell image data (206); and aspirating a cell meeting a preset sorting condition from the cells to be sorted according to the fluorescence signal intensities of the cells to be sorted (208).