ROI Annotation Detection in Pathology Images Using Color Segmentation
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Solution Overview
Problem
The manual annotation of regions of interest on digital slide images by pathologists is time-consuming and expensive, and existing methods often exclude hand-drawn pen annotations as artifacts, preventing their utilization in computational modeling.
Innovation Solution
A computer-implemented method and tool that detects and segments hand-drawn pen annotations on digitized pathology slides, converting them into digital masks for computational models, using a pipeline that includes color space conversion, thresholding, and morphological operations to extract and refine the annotation boundaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used to define regions of interest on digital slide images, then annotation accuracy can be achieved, but the process becomes time-consuming and expensive
Solution Approach 1:
The system enables automatic detection and extraction of pen annotations from digitized slides, allowing the annotation process to serve itself without requiring pathologist intervention. The computational model automatically identifies annotation regions, extracts boundary pixels, and generates ROI masks, eliminating the need for manual annotation while maintaining accuracy.
Solution Approach 2:
The patent replaces the mechanical manual annotation process with a computational image processing system. The system uses color space conversion, pixel selection algorithms, and morphological operations to automatically detect and extract annotations, substituting human manual work with automated computational methods that achieve comparable or superior accuracy.
2Device complexity
If hand-drawn pen annotations are excluded as artifacts, then image processing simplicity is maintained, but valuable annotation information is lost
Solution Approach 1:
The system extracts pen annotation information from the image by identifying pixels with annotation-specific color values. The extraction process separates annotation pixels from the rest of the image content, isolating the valuable annotation information while maintaining processing simplicity through automated detection and extraction algorithms.
Solution Approach 2:
The system changes the color space parameter to differentiate annotation pixels from other image content. By converting to a color space where annotation colors have distinct value ranges, the system can automatically identify and extract annotations based on color value thresholds, preserving annotation information while maintaining processing efficiency.
3Measurement precision
If color space conversion is applied to differentiate annotations from ROIs, then annotation detection accuracy improves, but computational complexity increases
Solution Approach 1:
The system changes the color space parameter to a format where annotation colors have distinct, easily identifiable value ranges. This parameter transformation enables accurate differentiation between annotation pixels and ROI pixels through simple threshold-based selection, improving detection accuracy while keeping computational complexity manageable through efficient color space conversion algorithms.
Data Source
AI summary
The present disclosure is directed to systems and methods for identifying regions of interest (ROIs) in images. A computing system may identify an image including an annotation defining an ROI. The image may have a plurality of pixels in a first color space. The computing system may convert the plurality of pixels from the first color space to a second color space to differentiate the annotation from the ROI. The computing system may select a first subset of pixels corresponding to the annotation based at least on a color value of the first subset of pixels in the second color space. The computing system may identify a second subset of pixels included in the ROI from the image using the first subset of pixels. The computing system may store an association between the second subset of pixels and the ROI defined by the annotation in the image.