Automated Digital Image Analysis for Simultaneous Cell Component Quantitation
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Solution Overview
Problem
Current digital image analysis techniques are inadequate for automated identification and quantification of nucleus, cytoplasm, and membrane in biological samples, leading to inefficiencies in cancer diagnosis and drug discovery, as they often require manual methods that are time-consuming and prone to errors.
Innovation Solution
A method and system for automatic digital image-based tissue-independent simultaneous nucleus, cytoplasm, and membrane quantitation, which involves identifying and classifying pixels from biological tissue samples to determine medical conclusions such as cancer diagnosis, using techniques like background pixel elimination, counterstained component removal, and simultaneous identification of cell components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used for identification and quantification of nucleus, cytoplasm, and membrane, then accuracy can be maintained, but time consumption increases and error susceptibility increases
Solution Approach 1:
The system enables automated self-analysis of digital images through algorithmic processing that automatically identifies and quantifies cellular components (nucleus, cytoplasm, membrane) without requiring manual intervention, thereby maintaining measurement precision while dramatically reducing time consumption
Solution Approach 2:
The patent replaces manual mechanical analysis methods with automated digital image processing systems that use computational algorithms to detect, segment, and quantify cellular structures, eliminating the need for human operators while preserving or enhancing measurement accuracy
2Reliability
If manual methods are used for analysis, then complex cellular structures can be carefully examined, but productivity decreases and human error increases
Solution Approach 1:
The automated system performs consistent, repeatable analysis of cellular structures through standardized algorithms, eliminating human variability and error while processing large numbers of images simultaneously, thereby improving both reliability and productivity
Solution Approach 2:
The system incorporates validation mechanisms that automatically verify analysis results, ensuring consistent quality control across all processed images and eliminating human error while maintaining high throughput capability
3Productivity
If existing automated techniques are used, then speed can be improved, but accuracy and tissue independence deteriorate
Solution Approach 1:
The patent develops a universal automated analysis system that can accurately process diverse tissue types and staining protocols through adaptive algorithms, achieving both high speed and high accuracy while being independent of specific tissue sources or staining methods
Solution Approach 2:
The system dynamically adjusts analysis parameters based on image characteristics and tissue type, enabling accurate quantification across different tissue sources and staining protocols while maintaining consistent processing speed through automated parameter optimization
4Speed
If existing automated techniques are used, then processing speed can be increased, but tissue independence and applicability across different sample types worsen
Solution Approach 1:
The patent creates a versatile automated analysis platform that maintains high processing speed while being adaptable to multiple tissue types and staining protocols through standardized, tissue-independent algorithms that automatically adjust to different sample characteristics
Data Source
AI summary
A method and system for automatic digital image based tissue independent simultaneous nucleus, cytoplasm and membrane quantitation. Plural types of pixels comprising cell components including at least cell cytoplasm and cell membranes from a biological tissue sample to which a chemical compound has been applied and has been processed to remove background pixels and pixels including counterstained components are simultaneously identified. The identified cell components pixels are automatically classified to determine a medical conclusion such as a human breast cancer, a human prostrate cancer or an animal cancer.