Machine Learning Cell Analysis Using Brightfield Imaging
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Solution Overview
Problem
Current methods for determining cell characteristics, such as viability, in biological samples through fluorescence imaging are complex and difficult to perform, despite the reliability of functional dyes.
Innovation Solution
A cell analysis apparatus and method that uses brightfield imaging in conjunction with a machine learning model derived from fluorescence and brightfield image pairs to determine cell characteristics, eliminating the need for complex fluorescence imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fluorescence imaging is used to determine cell characteristics, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent uses brightfield imaging as a simplified copy or alternative to fluorescence imaging. By training a machine learning model to translate brightfield images into accurate cell characteristic predictions, the system achieves fluorescence-level precision through a simpler imaging modality, effectively copying the useful outcome without the complex fluorescence setup
Solution Approach 2:
The patent changes the imaging parameter from fluorescence detection to brightfield detection. By using a machine learning model trained on paired images, the system transforms brightfield images (which lack fluorescence information) into accurate cell characteristic determinations, effectively changing the detection parameter while maintaining precision through computational intelligence
2Measurement precision
If fluorescence imaging is used to determine cell characteristics, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system copies the accurate cell characteristic determination capability of fluorescence imaging into a brightfield imaging framework. The machine learning model learns to replicate fluorescence-based measurements from brightfield images, making the process as simple as brightfield imaging while achieving fluorescence-level accuracy
Solution Approach 2:
The patent replaces the mechanical/optical complexity of fluorescence imaging with a computational approach. Instead of using fluorescence microscopy hardware and procedures, the system uses brightfield imaging combined with machine learning algorithms to achieve the same measurement precision, substituting computational processing for optical complexity
3Measurement precision
If machine learning model is trained using fluorescence and brightfield image pairs, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The machine learning model is trained in advance using paired fluorescence and brightfield images. This preliminary training action creates a pre-trained model that can then rapidly predict cell characteristics from brightfield images alone, transferring the time investment to a one-time setup phase rather than repeated measurements
Solution Approach 2:
The training process uses a large dataset of paired images (excessive action) to thoroughly train the model. By providing more training data than the minimum required, the system achieves robust generalization and high accuracy, making the model reliable across diverse cell types and conditions
Data Source
AI summary
There is provided a cell analysis apparatus that comprises image capture circuitry for capturing a brightfield image of a cell using brightfield imaging. The cell has been dyed by a functional dye that indicates, during fluorescence imaging and during brightfield imaging, whether the cell has a given characteristic. A model derived by machine learning is stored and used in combination with the brightfield image to determine whether the cell has the given characteristic. There is also provided a method for creating a cell categorisation model, comprising applying a functional dye to one or more samples comprising a plurality of cells. The functional dye indicates during fluorescence imaging and during brightfield imaging whether each of the cells has a given characteristic. A brightfield image and a corresponding fluorescence image for each of the plurality of cells to which the dye has been applied are captured and a machine learning process is used to generate a model that predicts whether a cell has the given characteristic from a brightfield image. The model is generated by using the brightfield image and the corresponding fluorescence image of each of the plurality of cells as training data.