Spectral Unmixing for Tissue Classification
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Solution Overview
Problem
Existing methods for classifying tissue samples, particularly in pathology and cytology, face challenges in distinguishing and separating multiple chromogenic or fluorescent stains with similar spectral absorption and emission characteristics, leading to difficulties in accurately determining the presence and distribution of different stains and structures within tissue samples.
Innovation Solution
A method involving spectral unmixing and classification using an image stack, where images are decomposed into unmixed sets corresponding to different spectral contributions, and a neural network is trained to classify pixels based on their provisional classifications, generating composite images to enhance contrast and accuracy, and classifying regions into respective classes using both spectral and spatial information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple chromogenic or fluorescent stains with similar spectral characteristics are used to highlight different structures in tissue samples, then the classification information and structural distinction are improved, but the difficulty of distinguishing and separating the stains increases
Solution Approach 1:
The patent applies segmentation by dividing the overlapping spectral information into separate components through spectral unmixing. The algorithm decomposes the mixed spectral signal from multiple stains into individual stain contributions, effectively separating the overlapping spectral characteristics and enabling distinct identification of each stain and its associated structure.
Solution Approach 2:
The patent transitions from analyzing stains in the spatial domain to analyzing them in the spectral domain. By acquiring and processing spectral information across multiple wavelengths, the system creates an additional dimensional space where stains with similar spatial distributions can be distinguished based on their unique spectral signatures, resolving the overlap problem.
2Ease of manufacture
If manual inspection and classification of tissue samples is performed, then the classification can be done with existing methods, but the process becomes tedious and time-consuming
Solution Approach 1:
The patent replaces the manual mechanical inspection process with an automated computational system. The machine-vision system captures spectral images and uses algorithms to automatically perform the classification tasks that previously required human observers to visually examine samples under microscopes, thereby eliminating the time-consuming manual inspection while maintaining classification capability.
Solution Approach 2:
The system enables self-service by allowing the tissue samples to be automatically classified without human intervention. The automated image analysis system performs the entire classification workflow independently, from image acquisition through spectral unmixing to final classification, making the process self-sufficient and eliminating the need for tedious manual inspection.
3Measurement precision
If spectral unmixing and automated classification methods are implemented, then the accuracy and speed of classification are improved, but the device complexity increases
Solution Approach 1:
The patent achieves multi-functionality by integrating multiple capabilities into a single automated system. The machine-vision system performs both image acquisition and spectral analysis, and the classification algorithm simultaneously handles spectral unmixing, feature extraction, and classification tasks, reducing the need for separate specialized devices while maintaining high accuracy.
Solution Approach 2:
The patent improves accuracy by changing the analytical parameters from simple spatial intensity measurements to comprehensive spectral parameter analysis. By measuring and analyzing multiple spectral parameters across different wavelengths, the system achieves higher classification precision while the automated processing manages the increased data complexity through efficient algorithms.
Data Source
AI summary
Methods are disclosed for classifying different parts of a sample into respective classes based on an image stack that includes one or more images.