CNN Cell Classification via Bright-Field Fluorescence Fusion
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Solution Overview
Problem
Existing methods for identifying cell morphology and quantifying biological substances using fluorescence images alone are prone to diagnostic errors due to the inability to accurately determine which fluorescent spots belong to individual cells or extraneous substances, especially in overlapping cell images.
Innovation Solution
An image processing apparatus and method utilizing a convolutional neural network (CNN) that combines bright-field and fluorescence images to extract features and generate classification information, including the type, morphology, and distribution of cells and biological substances by concatenating feature maps from both image types and performing hierarchical feature extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fluorescence images alone are used to identify cell morphology and quantify biological substances, then the quantification of fluorescent spots can be performed, but diagnostic errors occur due to inability to determine which fluorescent spots belong to individual cells
Solution Approach 1:
The patent combines bright-field images and fluorescence images into a single integrated image processing system. The bright-field image provides cell morphology information while the fluorescence image provides biological substance distribution information. By merging these two image types and processing them together through the same neural network, the system can accurately associate fluorescent spots with specific cells, thereby maintaining quantification accuracy while eliminating diagnostic errors.
2Reliability
If bright-field images and fluorescence images are manually compared to identify cell morphology and biological substances, then accurate identification can be achieved, but the operation becomes extremely complicated
Solution Approach 1:
The patent replaces the manual mechanical comparison process with an automated neural network-based image processing system. The convolutional neural network automatically performs feature extraction from both bright-field and fluorescence images, integrates the features, and generates classification information without requiring manual intervention. This substitution maintains high identification accuracy while dramatically simplifying the operation.
3Reliability
If manual comparison of bright-field and fluorescence images is performed, then accurate cell identification can be achieved, but the process becomes extremely time-consuming when observing large amounts of images
Solution Approach 1:
The patent replaces the slow manual comparison process with automated neural network processing that can rapidly analyze large numbers of images. The convolutional neural network performs parallel feature extraction and classification, generating results much faster than manual observation while maintaining the same high identification accuracy. This enables efficient processing of large datasets without sacrificing reliability.
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
Enables accurate and efficient identification and quantification of cell types and biological substances by correlating bright-field and fluorescence image features, reducing diagnostic errors and simplifying the image analysis process for large datasets.
Implementation Method 1
uses a convolutional neural network to extract respective image features of a bright-field image and a fluorescence image and to output classification information concerning the cell
Data Source
AI summary
Image processing apparatus 1 includes a hardware processor that acquires a bright-field image of a cell and a fluorescence image in which fluorescent spots of a fluorescent reagent applied dropwise to a region including the cell are imaged, and that uses a convolutional neural network to extract respective image features of the bright-field image and the fluorescence image and to output classification information concerning the cell.