Digital Pathology Image Analysis Optimization
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Solution Overview
Problem
Current digital pathology methods face challenges in efficiently managing large datasets from stained tissue samples, requiring laborious user involvement, lack of coordination between data acquisition and analysis, software incompatibility, and high personnel requirements, which hinders high-throughput analysis and data storage.
Innovation Solution
The methods and apparatuses optimize the histological process flow by iteratively identifying optimal parameters for image acquisition and analysis, generating optimized Whole Slide Image (WSI) data that includes selecting imaged regions, image acquisition parameters, contrast thresholds, and data compression values to simplify complex tissue images, allowing for accurate and quick analysis of large sets of slides.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If digital pathology methods are used to analyze stained tissue samples, then analysis speed and accessibility are improved, but data management complexity and storage requirements increase significantly
Solution Approach 1:
The patent extracts only the essential diagnostic information from whole slide images by generating segmented masks that isolate cells and regions of interest. This extraction process removes unnecessary image data while preserving critical diagnostic features, thereby reducing data management complexity without sacrificing analysis capability
Solution Approach 2:
The patent segments tissue samples into discrete cellular components and regions of interest using automated masking algorithms. This segmentation divides the complex continuous image data into manageable discrete elements, facilitating efficient storage, retrieval, and analysis while maintaining diagnostic accuracy
2Ease of operation
If manual slide analysis is performed, then data handling is simpler, but labor requirements and time consumption increase
Solution Approach 1:
The system performs automated image analysis and mask generation without requiring manual intervention at each step. The algorithms independently process whole slide images, segment cellular structures, and generate diagnostic masks, eliminating the need for continuous human operation while maintaining simplicity in data handling
Solution Approach 2:
The patent prepares and processes images in advance by generating segmented masks and extracting relevant features before final analysis. This preliminary processing reduces the time required during actual diagnostic review while keeping the interface simple for pathologists
3Loss of information
If high information content images with multiple biomarkers are acquired, then diagnostic information completeness is improved, but data storage and processing requirements increase
Solution Approach 1:
The patent extracts and preserves only the diagnostically relevant information from multiplexed images by creating segmented masks that highlight cells expressing specific biomarkers. This extraction maintains complete diagnostic information while representing it in a compact format that requires less storage space
Solution Approach 2:
The system transforms image data from continuous pixel intensity values to discrete segmented masks with defined boundaries and classifications. This parameter change from analog to digital representation maintains information completeness while significantly reducing data storage requirements
4Productivity
If coordinate transformation and rescaling are applied to images, then data transfer efficiency is improved, but image processing complexity increases
Solution Approach 1:
The patent performs coordinate transformations and rescaling operations during the initial image acquisition and mask generation phase. By completing these processing steps beforehand, the system enables efficient data transfer without requiring complex real-time processing during analysis
Data Source
AI summary
Digital pathology is a promising alternative to manual slide analysis, but improvements in imaging and analysis methods are needed to provide an analysis data set of images that can easily be handled, stored, and importantly, delivered from one data location to another. Methods and software are described herein to improve image collection and analysis.