Tumor Cell Isolines for Precise Tissue Dissection
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Solution Overview
Problem
Current methods for determining the tumor cell ratio (TCR) in tissue samples are inaccurate and labor-intensive, particularly for low-density tumor regions, as they rely on human estimation rather than automated cell counting and classification.
Innovation Solution
A method and system that utilize a classifier model to automatically locate and classify cells within a scanned tissue section, generate a TCR map, and create isolines to identify areas with a TCR at or above a target value, facilitating precise dissection of tissue samples for further analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated cell counting and classification is implemented, then measurement precision of tumor cell ratio is improved, but device complexity increases
Solution Approach 1:
The patent replaces manual mechanical cell counting with automated digital image analysis and machine learning classification. The system captures images of tissue sections and uses trained neural network models to automatically identify and classify tumor cells, eliminating the need for manual microscopic examination while significantly improving measurement precision and consistency.
Solution Approach 2:
The patent creates digital copies of tissue sections through high-resolution imaging, allowing virtual analysis and measurement without physically manipulating the original sample. This digital replication enables automated cell detection, classification, and ratio calculation while preserving the integrity of the physical tissue specimen.
2Productivity
If manual cell counting is performed, then device complexity is reduced, but productivity decreases due to labor-intensive process
Solution Approach 1:
The system performs self-service through automated image acquisition, processing, and analysis. The trained machine learning models independently identify and classify cells without requiring manual intervention for each cell count, enabling high-throughput analysis of multiple tissue sections while maintaining consistent quality standards.
Solution Approach 2:
The patent employs pre-trained machine learning models that have been previously trained on large datasets of labeled tissue images. This preliminary training action enables the system to immediately begin automated cell classification without requiring real-time human expertise, dramatically increasing processing speed and productivity.
3Measurement precision
If human estimation techniques are used, then device complexity is minimized, but measurement precision deteriorates due to inaccuracy
Solution Approach 1:
The system incorporates feedback mechanisms where the machine learning models continuously refine their classification accuracy by comparing predicted cell types against ground truth data from expert pathologists. This feedback loop enables the system to learn from errors and improve measurement precision over time, providing increasingly accurate tumor cell ratio determinations.
Solution Approach 2:
The patent utilizes multiple parameters including image intensity, cell morphology, nuclear characteristics, and spatial distribution to differentiate tumor cells from normal cells. By analyzing multiple parameters simultaneously rather than relying on single visual estimation, the system achieves superior measurement precision and accuracy in tumor cell ratio determination.
4Productivity
If isoline-based tissue selection is implemented, then productivity of sample preparation is improved, but device complexity increases
Solution Approach 1:
The patent extends the analysis from two-dimensional image space to three-dimensional tissue volume by generating isolines that represent constant tumor cell ratio values across the tissue section. This dimensional approach allows for precise identification and selection of tissue regions with specific TCR characteristics, dramatically improving sample preparation efficiency for downstream applications.
Data Source
AI summary
Methods and systems for processing a scanned tissue section include locating cells within a scanned tissue. Cells in the scanned tissue are classified using a classifier model. A tumor-cell ratio (TCR) map is generated based on classified normal cells and tumor cells. A TCR isoline is generated for a target TCR value using the TCR map, marking areas of the tissue section where a TCR is at or above the target TCR value. Dissection is performed on the tissue sample to isolate an area identified by the isoline.