Whole-Slide Image ROI Detection Through Multi-Scale Nuclear Segmentation
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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 multi-scale nuclear region concept is employed, using machine vision techniques like Region Growing and Floodfill to segment regions rich in hematoxylin stain and poor in eosin stain, combined with multi-scale ROI determination and SURF/RANSAC methods to identify and de-duplicate similar regions, ensuring accurate nuclear region counting across varying magnifications.
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 due to the large image size and number of regions to process
Solution Approach 1:
The patent segments the whole slide image into multiple tiles or regions of interest (ROIs) that can be processed independently. This segmentation allows the complex task of analyzing entire slide images to be divided into manageable units, reducing computational complexity while maintaining identification accuracy through systematic processing of individual regions.
Solution Approach 2:
The patent introduces magnification level as an additional dimension for processing. By analyzing images at multiple magnification levels (e.g., low magnification for overview, high magnification for detailed cancer foci identification), the system manages complexity through hierarchical processing while achieving precise cancer detection at the appropriate scale.
2Reliability
If multiple regions are selected for downstream analysis, then the chance to predict mutation improves, but processing time and computational resources increase
Solution Approach 1:
The patent changes parameters such as magnification level, region size, and staining intensity thresholds to identify and prioritize regions most likely to contain cancer foci. By adjusting these parameters systematically, the method selects a focused subset of high-value regions for downstream analysis, improving mutation prediction reliability while minimizing processing time through parameter-optimized region selection.
3Measurement precision
If regions rich in hematoxylin and poor in eosin are targeted, then cancer foci identification accuracy improves, but difficulty in detecting and measuring increases due to stain variability and image quality issues
Solution Approach 1:
The patent utilizes color deconvolution and histogram-based analysis to detect and quantify hematoxylin and eosin stain intensities. By transforming the problem into color space analysis and identifying characteristic color patterns associated with nuclear-rich regions, the method overcomes variability in staining and image quality to precisely identify cancer foci based on their distinctive hematoxylin-rich, eosin-poor color profile.
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.