Pathological Slide Subpatch Segmentation for Cell Component Analysis
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Solution Overview
Problem
Existing technologies struggle to comprehensively analyze pathological slide images, failing to accurately identify and segment all components of cells, such as cell membranes, cytoplasm, and nuclei, and quantify staining intensities, requiring manual expert intervention.
Innovation Solution
A computing apparatus and method that utilizes machine learning models to classify cells and tissues, segment pathological slide images into subpatches, and analyze components like cell membranes, cytoplasm, and nuclei, providing quantitative staining intensity scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning models are used to analyze pathological slide images, then the automation and efficiency of image analysis is improved, but the accuracy and completeness of identifying all cell components (cell membranes, cytoplasm, nuclei) deteriorates
Solution Approach 1:
The pathological slide image is divided into multiple patches, and each patch is further segmented into subpatches. This hierarchical segmentation allows the machine learning model to process manageable portions of the image while maintaining high accuracy in identifying cell components such as cell membranes, cytoplasm, and nuclei within each subpatch.
Solution Approach 2:
Different processing strategies are applied to different regions of the image based on their characteristics. The system identifies and prioritizes regions containing cell components, applying more rigorous analysis to these critical areas while maintaining overall automation. This allows the model to focus computational resources on achieving high precision where it matters most.
2Adaptability or versatility
If the entire pathological slide image is analyzed as a whole, then the comprehensive analysis capability is improved, but the processing time and computational complexity increases
Solution Approach 1:
The large pathological slide image is divided into multiple patches, which are further divided into subpatches. This hierarchical segmentation transforms a single complex processing task into multiple smaller, parallelizable tasks, significantly reducing processing time while maintaining comprehensive analysis capability across the entire image.
Solution Approach 2:
The system performs analysis on selected subpatches rather than processing every portion of the image with the same level of detail. By strategically selecting which subpatches require detailed analysis based on their content and importance, the system achieves comprehensive coverage without the excessive computational burden of uniform high-resolution processing throughout.
3Measurement precision
If detailed segmentation of cell components is performed, then the diagnostic accuracy is improved, but the device complexity and computational requirements increase
Solution Approach 1:
The image processing is segmented into multiple hierarchical levels (patches and subpatches), allowing detailed cell component analysis to be performed on small, manageable regions. This reduces the computational complexity of each individual processing unit while maintaining high diagnostic accuracy through cumulative analysis of all segments.
Solution Approach 2:
Detailed cell component segmentation and analysis is applied selectively to subpatches that contain relevant diagnostic information, rather than uniformly processing the entire image at maximum detail. This partial application of high-detail analysis reduces overall computational complexity while preserving diagnostic accuracy for critical regions.
Data Source
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AI summary
A computing apparatus includes a memory storing at least one program and a processor configured to perform at least one operation by executing the at least one program, wherein the processor is further configured to analyze a pathological slide image to classify at least one of cells and tissues included in the pathological slide image into at least one type, segment the pathological slide image into subpatches on the basis of a result of the classification, and analyze the subpatches to output information regarding components of a cell included in each of the subpatches.