Analyte Identification in Image Series via Signal Clustering
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Solution Overview
Problem
Existing methods for identifying analytes in image series face challenges with large data volumes, requiring significant memory and computing power, leading to high costs and long analysis times, and struggle to reliably detect analytes with signals slightly above the noise level.
Innovation Solution
A method using a cluster analysis algorithm to extract and cluster signal series from image areas, determining cluster centers, and assigning them to analytes or background based on distances, combined with a candidate extraction model for efficient recognition of candidate signal series, and a registration model for optimal image registration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large data volumes from multiple coloring rounds are stored and processed, then complete analyte identification is achieved, but memory requirements and computing power needs increase significantly
Solution Approach 1:
The patent extracts and stores only the essential information needed for analyte identification - specifically the signal series from multiple coloring rounds - rather than storing complete raw image data. This extraction approach maintains the reliability of analyte identification while significantly reducing the quantity of data that needs to be stored and processed.
2Productivity
If SSD hard drives are used for rapid data access, then analysis speed improves, but the limited write cycle capacity is quickly exhausted
Solution Approach 1:
The patent implements a strategy of discarding redundant data and recovering only the essential signal series information needed for analysis. By processing and reducing raw image data into compact signal series representations, the system minimizes repeated write operations to storage devices, thereby extending SSD lifespan while maintaining rapid data access capability for the condensed information.
3Measurement precision
If high computing power is allocated for analyzing large data volumes, then analysis accuracy improves, but system costs and energy consumption increase
Solution Approach 1:
The patent extracts only the relevant signal series from the complete image data, separating the essential information from redundant data. This extraction reduces the computational burden while preserving the precision needed for detecting analytes with signals slightly above noise level, thereby lowering energy consumption without sacrificing measurement precision.
4Reliability
If conventional methods are used to detect analytes, then standard analytes are identified, but analytes with signals near noise level are not reliably detected
Solution Approach 1:
The patent performs preliminary processing of image data from multiple coloring rounds to extract and consolidate signal series before final analyte identification. This preliminary action enhances the signal-to-noise ratio by aggregating information across multiple rounds, making it possible to reliably detect analytes with weak signals that would be indistinguishable from noise in single-round analysis.
Data Source
AI summary
A method for identifying analytes in an image series, the image series being generated by marking the analytes with markers in multiple coloring rounds and detecting the markers using a camera. The markers are selected in such a way that image signals of an analyte in an image area over the image series include colored signals and uncolored signals. The method comprises extracting multiple signal series of an image area of the image series in each case and filtering out candidate signal series from the extracted signal series. A ratio of at least one of the colored and/or uncolored signals of a candidate signal series to at least one other of the colored and/or uncolored signals of the particular signal series is a characteristic ratio, and/or a candidate signal series has a characteristic signature that has at least one characteristic ratio.