Tumor Cell Quantification via Multi-Resolution Image Segmentation
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Solution Overview
Problem
Current methods for identifying tumor regions in tissue samples are subjective and inaccurate, relying on visual estimation by pathologists, which can lead to inconsistent results due to the complexity of pathology images and the mixture of tumor and non-tumor cells.
Innovation Solution
A computerized image analysis system that uses multi-resolution analysis and object-based segmentation to identify tumor regions by processing high-resolution microscopic images, allowing for objective and reproducible measurements of tumor cell populations, including tumor cell size averaging and relative quantity estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual estimation by pathologist is used to identify tumor regions, then the method is simple and quick, but the measurement precision and reliability are poor due to subjectivity and image complexity
Solution Approach 1:
The patent replaces the manual visual estimation process (mechanical human operation) with an automated computerized image analysis system that uses multi-resolution analysis and object-based segmentation algorithms to objectively identify and measure tumor cell populations, thereby improving measurement precision while accepting increased system complexity
Solution Approach 2:
The patent applies segmentation by dividing the complex pathology image into multiple resolution levels and further segmenting tissue structures into discrete objects for analysis. This multi-resolution segmentation approach enables precise tumor region identification by analyzing images at different scales and separating tumor cells from non-tumor cells systematically
2Manufacturing precision
If manual annotation with marker pen is used to mark tumor regions, then the process is straightforward, but the manufacturing precision and reproducibility are poor due to human variability
Solution Approach 1:
The patent replaces the manual mechanical annotation process with an automated computerized system that uses image processing algorithms to objectively define tumor region boundaries, eliminating human variability and improving both precision and reproducibility of tumor region marking
Solution Approach 2:
The patent creates a digital copy of the tissue section image and performs all boundary definition and region marking operations on this digital replica, allowing for precise, reproducible, and editable tumor region delineation without physically altering the original slide
3Measurement precision
If high-resolution microscopic images are analyzed, then the measurement precision improves, but the processing time and computational resources increase
Solution Approach 1:
The patent reduces processing time by segmenting the high-resolution image analysis into multiple resolution levels, allowing rapid preliminary analysis at lower resolutions followed by detailed examination only of regions of interest, thereby maintaining measurement precision while significantly reducing overall processing time
Solution Approach 2:
The patent applies partial action by performing comprehensive high-resolution analysis only on selected regions of interest rather than the entire tissue section, using lower-resolution screening to identify areas requiring detailed examination, thus reducing total processing time while maintaining accuracy where needed
Data Source
AI summary
A computer implemented method is provided for determining an amount of tumor cells in a tissue sample. The method includes obtaining first image data describing an image of a tissue sample at a first resolution, obtaining second image data describing the image at a second resolution, wherein the first resolution is lower than the second resolution, selecting a candidate tumor region from the second image data based on texture data determined from the first image data, identifying a tumor structure in the candidate region of the second image data, and determining a number of cells in the tumor structure based on its area and an estimate of tumor cell area to estimate an amount of tumor cells in the tissue sample.