CNN-LSTM Cell Reprogramming Prediction via Probability Maps
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Solution Overview
Problem
The process of producing induced pluripotent stem (iPS) cells is time-consuming and labor-intensive, with low induction efficiency, and existing methods struggle to identify cells with reprogramming potential at an early stage, making it difficult to improve the speed and cost of the selection process.
Innovation Solution
A method using a combination of convolutional neural networks (CNN) and long short-term memory (LSTM) networks to predict the reprogramming status of cells from microscopic images by capturing region of interest (ROI) images, calculating probabilities, and producing predicted probability maps to determine the reprogramming status, which includes training CNN and LSTM models with specific templates and optimizing parameters for accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual methods are used to identify iPS cells, then the selection process can be performed with simple equipment, but the process is highly time-consuming and labor-intensive, taking several months or even years to identify potential iPS cells from numerous candidate colonies
Solution Approach 1:
The patent replaces manual visual inspection and mechanical cell selection methods with an automated image processing system that uses computer algorithms to analyze microscopic images, extract cell features, and predict reprogramming status. This substitution of mechanical/manual operations with automated computational methods dramatically reduces the time required from months to minutes while maintaining or improving selection accuracy.
Solution Approach 2:
The patent introduces an intermediate image processing system that acts as a mediator between the cell culture process and the final selection decision. This system captures images of cell colonies, processes them through algorithms that analyze morphological features, and generates predictions about which colonies are likely to become iPS cells, thereby streamlining the overall workflow and reducing time loss.
2Reliability
If traditional methods are used to identify reprogrammed cells, then the equipment and methodology remain simple, but the induction efficiency is extremely low, with only 0.001-0.1% of cells becoming iPS cells
Solution Approach 1:
The patent changes the parameters used for cell identification from simple visual inspection to a multi-parameter analysis system that evaluates multiple morphological features simultaneously. By analyzing several parameters (cell shape, size, texture, spatial distribution) through image processing algorithms, the system achieves high identification accuracy while managing complexity through systematic parameter evaluation.
Solution Approach 2:
The patent transitions from two-dimensional visual inspection to multi-dimensional analysis by extracting and evaluating multiple features from images (morphological parameters, textural properties, spatial relationships). This dimensional expansion allows the system to capture more information about cell characteristics, improving identification reliability without requiring proportionally increased device complexity.
3Loss of time
If existing machine learning methods like CNN are used to detect iPS cell colonies, then the identification speed improves, but these methods can only identify cells that have already reprogrammed or are undergoing reprogramming, not cells with reprogramming potential at an early stage
Solution Approach 1:
The patent applies preliminary action by analyzing cell morphological features at early time points in the reprogramming process to predict which cells will eventually become iPS cells. Rather than waiting for clear reprogramming markers to appear, the system performs preliminary identification based on subtle early changes in cell morphology, enabling earlier selection and reducing the time loss associated with waiting for definitive reprogramming evidence.
Data Source
AI summary
Disclosed herein are methods for predicting the reprogramming process of cells from a microscopic image of one or more cells. According to some embodiments, the method includes capturing an image of region of interest (ROI) of every pixel of the microscopic image, followed by processing the ROI image with a trained convolutional neural network (CNN) model and a trained long short-term memory (LSTM) network so as to obtain predicted probability maps. Also disclosed herein are a storage medium and a system for executing the present methods.