Whole Slide Image ROI Identification with Multi-Scale Nuclear Counting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Identifying regions of interest in whole slide images, particularly in the context of cancer detection, is challenging due to the presence of background noise, blurred regions, and variations in appearance at different magnifications, which complicates the identification of cancer foci.
Innovation Solution
A method that utilizes multi-scale nuclear region counting and machine vision techniques, such as Region Growing, Floodfill, and Region Withering, to segment and count nuclear regions at varying magnifications, combined with SURF and RANSAC for de-duplicating similar regions, ensuring accurate and interpretable ROI identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If whole slide images are analyzed at high magnification to identify cancer foci, then measurement precision improves, but device complexity and analysis time increase significantly
Solution Approach 1:
The patent segments the whole slide image analysis into multiple magnification levels (low, medium, high power fields). At low magnification, the system identifies candidate regions containing nuclear-rich areas. At high magnification, it precisely counts and characterizes nuclear regions within those candidates. This hierarchical segmentation allows accurate cancer foci identification while managing computational complexity by processing only relevant regions at each scale.
Solution Approach 2:
The patent adds the magnification level dimension to the analysis, transitioning from a single-scale approach to a multi-scale framework. By analyzing images at multiple magnifications (1.25x, 5x, 10x, 20x), the system captures both broad spatial context and fine nuclear details, resolving the contradiction between comprehensive coverage and detailed precision without requiring excessively complex single-scale processing.
2Productivity
If machine learning techniques are used for ROI identification, then analysis speed improves, but interpretability and clinical acceptability decrease
Solution Approach 1:
The patent implements automated quality control that performs self-assessment of image regions. The system automatically evaluates each candidate ROI for adequacy based on predefined criteria (nuclear region count, area ratios, quality metrics) and either accepts or rejects regions without human intervention. This self-service approach maintains high productivity while preserving interpretability through transparent, rule-based decision-making that clinicians can understand and verify.
3Loss of information
If multiple ROIs are selected to cover the entire slide, then representation completeness improves, but computational burden and analysis time increase
Solution Approach 1:
The patent applies partial action by selecting a representative subset of ROIs rather than analyzing every possible region. It identifies mode ROIs (representative examples) and random ROIs (for coverage) that together provide sufficient representation of the slide's nuclear architecture. This selective approach achieves adequate slide coverage while limiting the number of regions subjected to computationally intensive downstream analysis, thus balancing completeness with efficiency.
Data Source
AI summary
The present application relates generally to identifying regions of interest in images, including but not limited to whole slide image region of interest identification, prioritization, de-duplication, and normalization via interpretable rules, nuclear region counting, point set registration, and histogram specification color normalization. This disclosure describes systems and methods for analyzing and extracting regions of interest from images, for example biomedical images depicting a tissue sample from biopsy or ectomy. Techniques directed to quality control estimation, granular classification, and coarse classification of regions of biomedical images are described herein. Using the described techniques, patches of images corresponding to regions of interest can be extracted and analyzed individually or in parallel to determine pixels correspond to features of interest and pixels that do not. Patches that do not include features of interest, or include disqualifying features, can be disqualified from further analysis. Relevant patches can analyzed and stored with various feature parameters.