Deep Learning Cell Form Analysis Without Staining
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Solution Overview
Problem
Current methods for analyzing cell forms in microscopic images are insufficient for accurate and efficient identification, particularly for stem cells and differentiated cells intended for transplantation, as they fail to reliably discriminate cell forms without the need for staining or extensive human observation.
Innovation Solution
An image analysis method utilizing a deep learning algorithm with a neural network structure that generates and processes data from phase difference or differential interference contrast images to accurately identify cell forms, allowing for the discrimination of cell regions without staining, using training data from fluorescence images to learn cell characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image analysis methods are used to identify cell forms, then the process requires human observation and staining, but the accuracy and efficiency of cell form discrimination remain insufficient
Solution Approach 1:
The patent replaces the mechanical system of human visual observation with an automated deep learning-based image analysis system. The neural network algorithm automatically processes microscopic images to identify and discriminate cell forms, eliminating the need for manual observation while improving both accuracy and efficiency. The system uses convolutional neural networks to learn complex patterns in cell morphology without human intervention.
Solution Approach 2:
The patent transforms the analysis approach by changing from traditional staining-based methods to unstained cell imaging using phase-contrast or differential interference contrast microscopy. This parameter change in imaging technique, combined with deep learning algorithms, enables accurate cell form discrimination without the time-consuming staining process, thereby improving both precision and productivity.
2Reliability
If staining methods are used to determine cell characteristics, then cell identification can be performed, but the process becomes time-consuming and requires additional treatment steps
Solution Approach 1:
The patent applies preliminary action by training the deep learning model in advance using labeled training data. Once trained, the model can rapidly analyze cell images without requiring staining or additional treatment steps during the actual analysis phase. The complex pattern recognition is performed beforehand during training, enabling fast and reliable cell characteristic determination during deployment.
Solution Approach 2:
The patent replaces the chemical staining process with a computational approach using deep learning algorithms. Instead of using stains to make cells visible and distinguishable, the system uses neural networks to analyze optical contrast in unstained cells, eliminating the time-consuming staining steps while maintaining or improving identification reliability.
3Measurement precision
If human eyes are used to determine cell characteristics, then detailed observation is possible, but the enormous number of cells makes it difficult to check all cells
Solution Approach 1:
The patent implements self-service by enabling the deep learning system to automatically perform cell characteristic determination without human intervention. The trained neural network independently processes images, identifies cell forms, and determines characteristics, allowing the system to analyze enormous numbers of cells at speeds impossible for human observers while maintaining high accuracy through learned patterns.
Solution Approach 2:
The patent creates a universal deep learning model that can analyze various cell types and characteristics using the same framework. The neural network is trained on diverse data and can generalize to different cell forms, enabling it to handle enormous volumes of cell images across multiple cell types with consistent accuracy, far exceeding human analytical capacity.
Data Source
AI summary
An image analysis method according one or more embodiments may analyze a form of a cell using a deep learning algorithm with a structure of a neural network. The image analysis method may include: generating data for analysis from an image for analysis in which an analysis target cell is captured; inputting the data for analysis into the deep learning algorithm; and generating data indicating a form of the analysis target cell using the deep learning algorithm.