Pathological Slide Subpatch Segmentation for Cell Component Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies struggle to comprehensively analyze pathological slide images, failing to accurately identify and segment individual components of cells, such as cell membranes, cytoplasm, and nuclei, and quantify staining intensity, requiring manual intervention and reducing analysis accuracy.
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 and visualizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing machine learning models are used to analyze pathological slide images, then some objects can be identified, but comprehensive analysis of all objects and detailed components is not achieved
Solution Approach 1:
The pathological slide image is divided into multiple subpatches, and each subpatch is analyzed separately to identify different objects and components. This segmentation approach enables comprehensive analysis of all objects including cells, tissues, and their detailed components that would be missed in a whole-image analysis.
2Measurement precision
If manual intervention is used to identify and segment cell components, then accurate analysis can be achieved, but analysis time and complexity increase
Solution Approach 1:
The machine learning model automatically performs segmentation and identification of cell components without requiring manual intervention. The model processes the pathological slide images autonomously, identifying cells, tissues, and their components while eliminating the need for manual analysis, thus maintaining accuracy while significantly reducing analysis time.
3Loss of information
If detailed segmentation of cell components is performed, then comprehensive medical information can be extracted, but device complexity and processing requirements increase
Solution Approach 1:
The image is segmented into subpatches and further into cell components (cell membranes, cytoplasm, nuclei) through automated machine learning processes. This segmentation enables extraction of comprehensive medical information including staining intensity scores for each component without requiring complex manual processing systems.
Solution Approach 2:
The patent replaces manual mechanical analysis with automated machine learning-based image processing. The machine learning model performs segmentation, identification, and quantification of cell components, substituting the need for complex manual processing systems while maintaining or improving analysis accuracy.
Data Source
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.