Spectral Unmixing via Pixel Grouping for Microscopic Imaging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The spectral unmixing process in multiplex slides of biological tissue samples is computationally intensive, requiring extensive processing for each pixel, which slows down imaging systems and reduces throughput in healthcare environments.
Innovation Solution
The method involves identifying groups of similar pixels and reusing the unmixing results for these pixels, reducing the need for repeated execution of the unmixing algorithm by selecting a representative pixel and applying a scaling factor to obtain unmixing results for other similar pixels, thereby minimizing computational steps and processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spectral unmixing is performed on every pixel individually, then measurement precision is maintained, but processing time increases and productivity decreases
Solution Approach 1:
The patent merges pixels with similar spectral characteristics into groups, performing spectral unmixing once per group rather than once per pixel. This combining approach maintains measurement precision for all pixels in the group while dramatically reducing the total number of unmixing operations required, thereby resolving the contradiction between precision and productivity.
Solution Approach 2:
The patent creates a universal unmixing solution for groups of pixels sharing similar spectral properties. By identifying representative pixels and reusing their unmixing results across multiple similar pixels, the system achieves multi-functionality where one unmixing operation serves multiple pixels, thus improving throughput without sacrificing accuracy.
2Measurement precision
If spectral unmixing is performed on every pixel individually, then unmixing accuracy is maintained, but computational complexity increases
Solution Approach 1:
The patent reduces computational complexity by merging pixels into spectral groups, transforming the computational problem from O(N) individual pixel unmixing operations to O(M) group unmixing operations where M << N. This merging strategy maintains unmixing accuracy while dramatically simplifying the computational burden.
Solution Approach 2:
The patent performs preliminary spectral similarity assessment and grouping before executing the computationally intensive unmixing algorithm. By pre-identifying which pixels can share unmixing results, the system avoids redundant computations and reduces overall computational complexity while preserving accuracy through the reuse of validated unmixing results.
3Reliability
If spectral unmixing is performed on every pixel individually, then processing completeness is ensured, but processing time increases
Solution Approach 1:
The patent merges pixels into spectral groups and performs unmixing once per group, ensuring processing completeness for all pixels in the group through the representative unmixing result. This approach maintains reliability by covering all pixels while reducing total processing time by eliminating redundant unmixing operations across similar pixels.
Solution Approach 2:
The patent creates copies of unmixing results from representative pixels and applies them to similar pixels within the same spectral group. This copying mechanism ensures that every pixel receives a processed result (maintaining completeness) while avoiding the time cost of re-computing unmixing for each pixel individually, thus resolving the time-completeness contradiction.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Systems and methods for speeding up a spectral unmixing process by using pixel groups are disclosed. Operations including forming groups of similar pixels, and unmixing only one representative pixel from each pixel group enable determination of an unmixing result for all the pixels in the group. A similarity metric may be based on a dot product of an unprocessed pixel with the representative pixel in the subset of pixels. The method is repeated for any number of remaining unmatched pixels that exceed a threshold, until the number of remaining or unmatched pixels is smaller than the threshold, upon which the remaining pixels may be individually unmixed. Significantly fewer unmixing operations are performed on an image, thereby speeding up the unmixing process.