Pathological Diagnosis Support Using Cell Nuclei Positioning
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Solution Overview
Problem
Existing pathological diagnosis support apparatuses are insufficient in providing comprehensive support for distinguishing between cancer cells and benign tumor cells, as they lack information on the structural features of tissue samples and specific cell types, such as signet ring cells, which are crucial for accurate diagnosis.
Innovation Solution
A pathological diagnosis support apparatus and method that extracts cell nucleus, cytoplasm, and glandular cavity areas from digital color images, measures their basic and structure feature quantities, and identifies specific types of areas like signet ring cells, mucus, and foreign bodies, displaying these features to aid experts in diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If basic feature quantities (area, circumferential length, degree of circularity) are measured for cell nucleus, cytoplasm, and glandular cavity areas, then the diagnostic information completeness is improved, but the device complexity increases due to multiple extraction and measurement processes
Solution Approach 1:
The image processing is divided into distinct segmentation steps: first extracting cell nucleus areas based on blue luminance values, then cytoplasm areas based on red luminance values, and finally glandular cavity areas based on white luminance values. This segmentation allows each tissue component to be independently measured for basic feature quantities (area, circumferential length, degree of circularity) without interference, comprehensively capturing diagnostic information while managing complexity through systematic division of the processing task.
2Measurement precision
If structure feature quantities representing positioning manners of cell nuclei are measured, then the ability to distinguish cancer cells from benign tumor cells is improved, but the measurement and processing complexity increases
Solution Approach 1:
The patent extracts and emphasizes structure feature quantities that specifically represent the positioning manners of cell nuclei within tissue samples. By taking out this particular feature from the overall image analysis, the system can focus on measuring spatial relationships and arrangement patterns of cell nuclei, which are critical for distinguishing cancer cells from benign tumor cells. This extraction approach improves measurement precision for cancer differentiation while managing complexity by concentrating on specific diagnostic features rather than analyzing all possible image characteristics.
3Measurement precision
If specific types of areas (signet ring cells, mucus, foreign bodies) are identified and extracted, then the diagnostic accuracy is improved, but the processing time and computational load increase
Solution Approach 1:
The system performs preliminary identification of specific tissue components (signet ring cells, mucus, foreign bodies) during the initial image processing stage by analyzing luminance value distributions and basic feature quantities. By identifying these specific areas early in the processing workflow rather than in subsequent separate analyses, the system improves diagnostic accuracy through comprehensive detection while reducing overall processing time. The preliminary action allows integration of specific component identification into the existing extraction and measurement framework, avoiding redundant processing steps.
Data Source
AI summary
A pathological diagnosis support apparatus into which a digital color image showing a stained tissue sample is input, the apparatus including: a display that performs a display operation; and an image processor that when the digital color image is input, extracts cell nucleus areas, cytoplasm areas and glandular cavity areas, respectively, based on luminance values of pixels of the digital color image, measures basic feature quantities representing features of shapes of the respective cell nucleus areas, cytoplasm areas and glandular cavity areas, which have been extracted, determines whether or not a particular kind of area that appears in a limited case according to the disease state of the tissue sample exists, for each of the cell nucleus areas, the cytoplasm areas and the glandular cavity areas based on the luminance values and the basic feature quantities, and measures structure feature quantities representing ways in which the cell nucleus areas are positioned, based on the basic feature quantities of the cell nucleus areas, and if the particular kind of area exists, extracts the particular kind of area and makes the display show it together with the structure feature quantities and the cell nucleus areas.