Thyroid Smear 3D Cell Cluster Counting With Circle-Based Estimation
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Solution Overview
Problem
The existing methods for determining the total number of cells in a three-dimensional cell cluster in thyroid puncture smears are inaccurate due to irregular contours and inconsistent cell numbers caused by external forces, leading to difficulties in accurately classifying these clusters as 'microfollicles' or 'medium follicles', which affects diagnostic accuracy for follicular neoplasm.
Innovation Solution
A method involving drawing a big circle around the cell cluster, adjusting it to ensure small circles representing follicular cells completely surround it, marking cell-free areas, and using a model to count overlapping and identifiable cells, with an upper limit of 70 cells for 'microfollicles', to determine the total cell count accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional visual counting methods are used to determine cell cluster type, then the classification process is simple and quick, but the accuracy is low due to irregular contours and inconsistent cell numbers caused by external forces
Solution Approach 1:
The cell cluster is segmented into multiple regions by drawing concentric circles (first circle, second circle, third circle) around the cluster. Each region is counted separately, with the innermost region containing the three-dimensional cell cluster and outer regions containing two-dimensional cell clusters. This segmentation allows systematic counting and reduces errors from irregular contours.
Solution Approach 2:
The method transitions from two-dimensional visual estimation to a multi-dimensional approach by establishing a mathematical model that considers radial distance from the center. The model uses the formula N = k × (R² - r²) to calculate cell numbers based on radial dimensions, transforming the complex two-dimensional counting problem into a one-dimensional radial calculation problem.
2Reliability
If the upper limit of cell numbers for microfollicle classification is set strictly, then the diagnostic criteria are clear and objective, but many clusters with irregular shapes cannot be accurately classified
Solution Approach 1:
The method changes the parameter for classification from fixed cell number counts to cell density parameters. By calculating cell density (cells per unit area) and using density thresholds rather than absolute cell numbers, the classification adapts to clusters of different sizes and shapes. The model determines whether a cluster is microfollicle or follicle based on density parameters that remain consistent regardless of cluster irregularity.
3Measurement precision
If manual counting of all cells in a cluster is performed, then the total cell number is accurate, but the process is time-consuming and prone to human error
Solution Approach 1:
The method performs preliminary actions by establishing the mathematical model and determining cell density parameters before actual classification. The model N = k × (R² - r²) is pre-established with calibrated constants, allowing rapid calculation of cell numbers once the radial dimensions are measured. This preliminary model establishment enables efficient processing of multiple clusters without repeated manual counting.
Solution Approach 2:
The method replaces the mechanical manual counting process with a mathematical calculation system. Instead of physically counting each cell, the model calculates cell numbers based on measured radial dimensions and pre-determined density constants. This substitution of mechanical counting with mathematical computation significantly improves efficiency while maintaining accuracy.
Data Source
AI summary
Provided is a method for establishing an estimation model of a total number of cells of a three-dimensional cell cluster in a thyroid puncture smear. The method for establishing the estimation model of the total number of the cells of the three-dimensional cell cluster in the thyroid puncture smear includes following steps: S1: selecting a three-dimensional cell cluster; S2: first drawing a big circle, and enclosing the three-dimensional cell cluster into the big circle; S3: drawing small circles with actual diameters of follicular cells, and surrounding the small circles around the big circle; adjusting a diameter of the big circle properly during a drawing process, making the small circles completely surround the big circle with a total number of the small circles are divisible by 4; and S4: marking black circles with a same size as the small circles to supplement cell-free areas.