Automated Cell Classification via Feature Extraction
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Solution Overview
Problem
Manual annotation for cell classification in biological samples is laborious, time-consuming, and prone to individual bias, making accurate and reproducible recognition of cellular patterns challenging, especially in complex tissue microenvironments.
Innovation Solution
An automated system that processes image data to generate object identifiers, extract intensity and shape features, and assign objects to groups using threshold values and feature vectors, eliminating the need for manual classification and enabling efficient classification of cell phenotypes and sub-phenotypes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used for cell classification, then classification accuracy can be maintained, but the process is laborious and time-consuming
Solution Approach 1:
The system performs self-service by automatically generating training sets and classifying cells without requiring manual annotation. The automated algorithm processes images, extracts features, and assigns cell types independently, eliminating the time-consuming manual intervention while maintaining classification accuracy through computational methods.
Solution Approach 2:
The patent replaces the mechanical manual annotation process with an automated computational system. Instead of human observers manually identifying and annotating cells, the system uses image processing algorithms, feature extraction, and machine learning models to automatically classify cells, substituting human labor with automated mechanical processes.
2Measurement precision
If manual annotation is used for cell classification, then detailed phenotypic analysis is possible, but individual bias affects results
Solution Approach 1:
The system changes the parameters of cell classification from subjective human judgment to objective quantitative measurements. By extracting numerical features such as intensity values, shape descriptors, and spatial characteristics, the system transforms phenotypic analysis into measurable parameters that can be consistently reproduced without human bias, thereby improving reliability while maintaining detailed analysis capability.
3Productivity
If automated classification is implemented, then time and cost are reduced, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex classification task into distinct manageable steps: image acquisition, feature extraction, data preprocessing, model training, and classification. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while maintaining high productivity through automated processing.
4Measurement precision
If manual cell counting is performed, then accuracy can be maintained for simple samples, but it becomes difficult in complex tissue microenvironments
Solution Approach 1:
The system introduces an intermediary feature extraction layer that bridges the gap between raw images and final classification. By extracting intermediate features such as intensity values, shape descriptors, and spatial relationships, the system simplifies the complex tissue microenvironment into manageable characteristics that can be reliably detected and measured, enabling accurate cell counting even in complex samples.
Data Source
AI summary
The subject matter of the present disclosure generally relates to techniques for image analysis. In certain embodiments, various morphological or intensity-based features as well as different thresholding approaches may be used to segment the subpopulation of interest and classify object in the images.