Cell Classification Using Individual and Massive Cell Detection
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Solution Overview
Problem
Existing cell classification methods, such as those described in Patent Literature 1 and 2, are limited in accuracy and require specific staining and imaging techniques, making them unsuitable for rapid and accurate classification of specimen cells as benign or malignant without the need for invasive specimen collection.
Innovation Solution
A cell classification apparatus and method that includes an acquisition means to capture specimen cells, a determination means to differentiate between individual and massive cells, and a classification means to classify these cells as benign or malignant, utilizing machine learning models trained on individual and massive cell images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If professional knowledge and experience are used to determine whether a collected cell is benign or malignant through ROSE, then diagnosis accuracy is improved, but implementation is limited due to limited human resources of skilled persons
Solution Approach 1:
The patent replaces the mechanical system of human expert observation and judgment with an automated image processing and classification system. The system uses image acquisition means to capture cell images, determination means to identify individual cells, and classification means to automatically classify them as benign or malignant, substituting human professional knowledge with computational algorithms.
Solution Approach 2:
The classification system enables self-service by allowing unskilled personnel to perform cell classification without requiring expert knowledge. The automated determination and classification means provide the diagnostic capability that previously required skilled pathologists, making the service accessible to anyone operating the system.
2Adaptability or versatility
If machine assistance is used to allow unskilled persons to make diagnosis, then implementation availability is improved, but diagnosis accuracy may be reduced compared to expert judgment
Solution Approach 1:
The patent segments the diagnostic process into distinct functional components: image acquisition means for capturing cell images, determination means for identifying individual cells from the images, and classification means for categorizing cells as benign or malignant. This segmentation allows each component to be optimized independently while working together to achieve accurate automated diagnosis.
Solution Approach 2:
The classification means uses determination results (such as cell morphology parameters, size, shape, and other measured features) as input to classify cells. By changing from subjective expert judgment to objective parameter-based classification, the system achieves consistent and reproducible results that can be implemented widely without requiring expert knowledge.
3Reliability
If invasive specimen collection by puncture is performed multiple times to ensure suitable tissue cells are obtained, then diagnosis reliability is improved, but patient risk and discomfort increase
Solution Approach 1:
The patent applies preliminary action by performing rapid classification of collected cells before final pathological diagnosis. The image acquisition, determination, and classification processes are executed immediately on collected specimen cells to determine suitability for diagnosis, allowing assessment of specimen quality before committing to invasive procedures or rejecting adequate specimens.
Solution Approach 2:
The system provides feedback by classifying specimen cells and determining whether they are suitable for pathological diagnosis. This feedback mechanism allows immediate assessment of specimen quality, enabling operators to adjust collection procedures or attempt additional collections based on classification results, thereby improving overall diagnosis reliability while minimizing unnecessary invasive procedures.
Data Source
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AI summary
In order to subject a specimen cell to classification as benign or malignant without any limitation to a specific method, a cell classification apparatus (1) includes: an acquisition means (10) for acquiring an image that includes at least one specimen cell as a subject; a determination means (20) for determining whether each of the at least one specimen cell is an individual cell or a massive cell; and a classification means (30) for carrying out at least one selected from the group consisting of (i) a process for subjecting the at least one specimen cell that has been determined to be the individual cell to classification as benign or malignant and (ii) a process for subjecting the at least one specimen cell that has been determined to be the massive cell to classification as benign or malignant.