Digital Pathology Image Segmentation for Storage Efficiency
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Solution Overview
Problem
Current digital pathology methods face challenges in accurately analyzing and storing data from irregularly-shaped biological structures like fibroblasts or macrophages, as high-resolution analysis is resource-intensive and low-resolution analysis can lead to loss of accuracy due to heterogeneity in stained cells.
Innovation Solution
A mid-resolution analysis approach is employed, segmenting images into sub-regions with similar properties such as texture, intensity, or color, generating representational objects, and storing their coordinates and feature metrics in a database, allowing for efficient storage and retrieval of analysis results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution analysis is used to analyze irregularly-shaped biological structures, then measurement precision is improved, but device complexity and computational resources increase
Solution Approach 1:
The patent segments the image into multiple tiles or regions, analyzing each region separately. This divides the complex high-resolution analysis task into smaller manageable segments that can be processed more efficiently while maintaining overall analysis accuracy for irregularly-shaped structures.
Solution Approach 2:
The patent applies different analysis resolutions to different regions of the image based on local characteristics. High-resolution analysis is applied only to regions containing irregularly-shaped biological structures that require precise measurement, while other regions use lower resolution analysis, optimizing computational resources.
2Measurement precision
If high-resolution analysis is used to analyze irregularly-shaped biological structures, then measurement precision is improved, but storage space increases
Solution Approach 1:
The patent extracts only the essential high-resolution features and measurements from regions containing irregularly-shaped structures, storing only the critical data rather than the entire high-resolution image. This reduces storage requirements while preserving measurement precision for the structures of interest.
Solution Approach 2:
The patent transitions from storing full high-resolution image data to storing extracted feature data in a different dimensional representation. By converting spatial image data into feature vectors and measurements, storage requirements are dramatically reduced while maintaining analytical precision.
3Device complexity
If low-resolution analysis is used to reduce storage space and computational complexity, then device complexity is reduced, but measurement precision deteriorates due to heterogeneity in stained cells
Solution Approach 1:
The patent implements a dynamic resolution adjustment mechanism that adapts the analysis resolution based on the local image content. When irregularly-shaped structures are detected, the system dynamically increases resolution for those specific regions while maintaining lower resolution elsewhere, balancing computational efficiency with measurement precision.
4Quantity of substance
If low-resolution analysis is used to reduce storage space, then storage space is reduced, but measurement precision deteriorates due to heterogeneity in stained cells
Solution Approach 1:
The patent segments the image into multiple tiles or regions, analyzing each region separately. This divides the complex high-resolution analysis task into smaller manageable segments that can be processed more efficiently while maintaining overall analysis accuracy for irregularly-shaped structures.
Solution Approach 2:
The patent applies different analysis resolutions to different regions of the image based on local characteristics. High-resolution analysis is applied only to regions containing irregularly-shaped biological structures that require precise measurement, while other regions use lower resolution analysis, optimizing computational resources.
Data Source
AI summary
The present disclosure is directed, among other things, to automated systems and methods for analyzing, storing, and/or retrieving information associated with biological objects having irregular shapes. In some embodiments, the systems and methods partition an input image into a plurality of sub-regions based on localized colors, textures, and/or intensities in the input image, wherein each sub-region represents biologically meaningful data.