Computer-Assisted Tissue Analysis Using Hyperspectral Regression Models
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Solution Overview
Problem
Current methods for analyzing tissue sections using mass spectrometry data fail to effectively replace conventional histological stain tests, as they cannot efficiently extract and utilize the information contained in hyperspectral data for diagnostic purposes.
Innovation Solution
A method for computer-assisted analysis of tissue sections using hyperspectral data, specifically through steps of reading in spatially resolved hyperspectral data, creating a digital mask, determining base spectra, correlating these with the data, and establishing a regression model to enable digital stain tests, allowing for the identification of tissue zones with specific clinical pictures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If mass spectrometric methods are used to capture full spectrum data for each pixel, then comprehensive molecular information is obtained, but the data cannot be extracted effectively to replace conventional histological stain tests
Solution Approach 1:
The patent introduces an intermediary computational framework that includes: (1) training a classification model on reference mass spectrometry data paired with ground truth diagnoses, (2) extracting molecular features from hyperspectral data using the trained model, and (3) mapping these features to diagnostic categories. This intermediary processing pipeline transforms raw mass spectrometry data into diagnostically meaningful information that can replace conventional staining methods.
Solution Approach 2:
The patent transforms the mass spectrometry data by changing its parameter representation: (1) converting raw spectral data into feature vectors through dimensionality reduction, (2) normalizing intensity values across different measurement positions, and (3) mapping spectral parameters to diagnostic categories. These parameter transformations enable the data to be used for diagnostic purposes comparable to conventional histology.
2Ease of operation
If conventional histological stain tests are used, then visual identification of biomarkers is achieved, but biochemical procedures and physical staining are required
Solution Approach 1:
The patent replaces the mechanical and chemical staining procedures with a computational system: (1) substituting physical antibody deposition with computational classification models, (2) replacing chemical staining reactions with algorithmic feature extraction, and (3) converting wet-lab procedures into dry computational analysis. This substitution eliminates the need for complex biochemical procedures while maintaining diagnostic capability.
Solution Approach 2:
The patent creates a digital copy of the diagnostic process: (1) generating virtual stains through computational classification instead of physical staining, (2) creating digital representations of tissue zones with specific clinical pictures, and (3) reproducing diagnostic information in digital form that can be analyzed without physical manipulation of tissue sections.
3Productivity
If digital stain tests are implemented using computational methods, then conventional staining can be replaced, but effective extraction and utilization of hyperspectral data information is not achieved
Solution Approach 1:
The patent performs preliminary actions to enable effective information extraction: (1) pre-training classification models on reference datasets before analyzing new tissue sections, (2) pre-processing mass spectrometry data through normalization and feature selection, and (3) establishing diagnostic category mappings in advance. These preliminary steps ensure that when hyperspectral data is analyzed, the information can be extracted efficiently and accurately without loss of diagnostic value.
Data Source
AI summary
A method for computer-assisted analysis of one or more tissue sections of the human or animal body for preparing a digital stain test is provided. During the digital stain test, tissue zones in a tested tissue section with a predetermined clinical picture are detected. Spatially resolved hyperspectral data, particularly mass spectrometry data, is processed for a plurality of measurement positions. Multiple base spectra are determined from the hyperspectral data by obtaining base vectors. The base spectra are correlated with the hyperspectral data such that a plurality of correlation values for the base spectra is obtained for each measurement position. Subsequently, a regression model is calculated using a regression method, the regression model describing a mask of the one or more tissue sections, in which mask diseased tissue zones are marked based on the calculated correlation values. The regression model and the base spectra can then be used to conduct the digital stain test.