Cell Image Analysis Using Tone Vectors for Rare Morphology Detection
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Solution Overview
Problem
Existing methods for cell identification, such as microscopic observation and flow-type automatic hemocyte classification, struggle with low accuracy in identifying cells with low emergence frequencies and require significant labor for training data generation, limiting the ability to differentiate between similar morphologies and abnormal findings.
Innovation Solution
An image analysis method using a deep learning algorithm with a neural network structure to calculate the probability of cell morphology classifications, enabling automated identification of cell types and abnormal findings without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning technique is used for cell identification, then automation is improved, but analysis accuracy and generalization capability deteriorate due to limited training data
Solution Approach 1:
The patent uses tone vector data that copies and represents the essential visual characteristics of cell images in a condensed numerical format. This allows the machine learning model to work with efficient numerical representations while maintaining the key morphological information needed for accurate cell identification, thus improving automation without sacrificing accuracy.
Solution Approach 2:
The patent transforms cell image data into tone vector parameters that capture brightness and hue information. This parameter transformation enables the machine learning model to process cell morphology data efficiently while maintaining high accuracy in distinguishing different cell types, resolving the contradiction between automation and measurement precision.
2Reliability
If user creates training data manually, then model training is achieved, but labor and time consumption increase significantly
Solution Approach 1:
The system automatically generates tone vector data from cell images without requiring manual user intervention for each data point. The automated processing converts images to tone vectors and prepares training data independently, significantly reducing the time and labor needed for training data generation while maintaining model training reliability.
Solution Approach 2:
The patent replaces the manual mechanical process of creating training data with an automated computational system that converts cell images to tone vector representations. This substitution eliminates the time-consuming manual work while preserving the essential training capability, resolving the contradiction between reliability and time loss.
3Productivity
If flow-type automatic hemocyte classification apparatus is used, then examination throughput is improved, but identification capability for low emergence frequency cells deteriorates
Solution Approach 1:
The patent extracts tone vector features from cell images that capture morphological characteristics in a different dimensional space. This allows the system to maintain high throughput while improving identification capability for rare cell types by utilizing comprehensive tone vector representations that preserve subtle morphological differences.
Solution Approach 2:
The system uses tone vector parameters to represent cell morphology, transforming the data into a form that maintains both processing efficiency for high throughput and discriminative power for identifying low emergence frequency cells. This parameter transformation resolves the contradiction between productivity and measurement precision.
Data Source
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AI summary
Disclosed is an image analysis method including: inputting analysis data including information regarding an analysis target cell to a deep learning algorithm having a neural network structure; and analyzing an image by calculating, by use of the deep learning algorithm, a probability that the analysis target cell belongs to each of morphology classifications of a plurality of cells belonging to a predetermined cell group.