Estimator Learning Device for Cancer Detection Using Absorbance
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Solution Overview
Problem
Diagnostic pathology faces challenges in observing colorless and transparent cells and tissue specimens, which are often difficult to visualize, necessitating staining for microscopic observation, and there is a need for effective estimation of cancer presence in stained images using hyperspectral imaging.
Innovation Solution
An estimator learning device that acquires stained images in multiple wavebands, extracts cell nucleus regions, calculates absorbance, and trains an estimator to determine whether the cell nucleus is in a prescribed state based on the relationship between absorbance and sample information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If staining is performed on cells and tissue specimens to facilitate microscopic observation, then the visibility and observability of the specimens is improved, but the complexity of the diagnostic process increases and additional processing time is required
Solution Approach 1:
The patent extracts and analyzes the absorbance characteristics of cell nuclei in the stained regions from the overall image, separating the diagnostic information extraction from the staining process itself. This allows the system to leverage the staining-induced contrast while focusing computational analysis on specific nuclear properties in waveband images.
Solution Approach 2:
The patent introduces absorbance calculation as an intermediary step between image acquisition and cancer estimation. By converting raw image data into absorbance values that quantify the staining effect, the system creates a standardized intermediate representation that facilitates automated analysis without requiring direct interpretation of stained image patterns.
2Measurement precision
If stained images in multiple wavebands are acquired for cancer estimation, then the accuracy of cancer detection is improved, but the amount of data to be processed and the complexity of analysis increases
Solution Approach 1:
The patent extracts cell nucleus regions from the multi-waveband images and calculates absorbance specifically for these nuclear regions. This extraction focuses the analysis on the most diagnostically relevant areas, reducing the effective data volume from entire tissue sections to specific nuclear regions while maintaining diagnostic accuracy.
Solution Approach 2:
The patent applies different analytical approaches to different regions: absorbance calculation is applied specifically to cell nucleus regions rather than uniformly to all image areas. This localized approach tailors the analysis to the specific diagnostic needs of nuclear morphology assessment while reducing computational burden on non-relevant areas.
3Loss of information
If absorbance calculation is performed for each waveband in cell nucleus regions, then the diagnostic information quality is improved, but the computational processing time and complexity increase
Solution Approach 1:
The patent performs preliminary extraction of cell nucleus regions before conducting absorbance calculations across multiple wavebands. This preliminary segmentation prepares the data structure in advance, allowing subsequent absorbance calculations to be performed efficiently on pre-identified nuclear regions rather than requiring full-image analysis for each waveband.
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 estimation of whether a cell nucleus in a stained image is in a prescribed state, improving the diagnostic capability in cancer detection by enhancing the visualization and analysis of pathological specimens.
Implementation Method 1
calculates, for each of the stained images, an absorbance in each of the wavebands in the cell nucleus region
Data Source
AI summary
The estimator learning device contains an image acquisition unit that acquires stained images provided by photographing respectively in a plurality of wavebands a biomaterial sample that has been stained with a prescribed staining solution; a cell nucleus extraction unit that extracts a cell nucleus region present in the biomaterial sample in each of the stained images; a color information acquisition unit that calculates, for each of the stained images, an absorbance in each of the wavebands in the cell nucleus region; and an estimator learning unit that, based on a relationship between the absorbance in each of the wavebands and information associated with the biomaterial sample and relating to whether the cell nucleus present in the biomaterial sample is in a prescribed state, trains an estimator that estimates whether the cell nucleus is in the prescribed state from the absorbance in each of the wavebands.