Color Gamut Normalization for Pathology Slides
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Solution Overview
Problem
Current methods for digitizing and examining histological slides are time-consuming and limited, particularly with whole slide imaging (WSI), which faces challenges during digitization and visual examination, necessitating improved techniques for color gamut normalization and segmentation.
Innovation Solution
A system and method utilizing a computing device to apply segment-specific transformations to whole slide images based on biological tissue type variabilities and magnification levels, leveraging machine learning algorithms and computer vision techniques for automated color gamut normalization and segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual examination of histological slides using microscopes is used, then diagnostic accuracy can be maintained, but the process is time-consuming and labor-intensive
Solution Approach 1:
The system performs self-service by automatically segmenting tissue regions and applying color gamut normalization without requiring manual pathologist intervention for these preprocessing tasks, thereby reducing time loss while maintaining diagnostic accuracy
Solution Approach 2:
The system performs preliminary actions by automatically segmenting slides into tissue regions and normalizing colors before presentation to the pathologist, preparing the images in advance so that the actual diagnostic review can proceed more quickly
2Ease of operation
If whole slide imaging (WSI) is implemented for digitization, then image accessibility and storage are improved, but color consistency and visual examination quality deteriorate
Solution Approach 1:
The system applies local quality by implementing segment-specific color gamut normalization, where different color transformation parameters are applied to different tissue segments based on their individual color characteristics, thereby maintaining color consistency across the entire slide while preserving local tissue-specific color information
Solution Approach 2:
The system changes parameters by dynamically adjusting color gamut parameters (such as brightness, contrast, and color balance) for each segmented tissue region based on its specific color profile, ensuring that color consistency is maintained across diverse tissue types and magnification levels
3Device complexity
If uniform color transformation is applied to entire slides, then processing simplicity is maintained, but tissue-specific color variations and diagnostic details are lost
Solution Approach 1:
The system applies segmentation by dividing the entire slide into multiple tissue-specific segments based on histological features, allowing different color transformation parameters to be applied to each segment, thereby preserving tissue-specific color information while managing complexity through automated region classification
Solution Approach 2:
The system implements local quality by applying different color transformation characteristics to different tissue segments rather than using a uniform transformation across the entire slide, ensuring that diagnostic color details are preserved in each specific tissue region
4Manufacturing precision
If multiple color transformations are applied to different regions, then color accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The system changes parameters by implementing adaptive color transformation parameters for each tissue segment based on its specific color characteristics, achieving high color normalization accuracy while managing complexity through automated parameter selection and efficient computational algorithms
Data Source
AI summary
A system for color gamut normalization for pathology slide is disclosed. The system includes at least a computing device, wherein the computing device is configured to generate a plurality of segmentations of a whole slide image, wherein the whole slide image includes a plurality of biological tissue type variabilities. The computing device is configured to apply a segment-specific transformation to an individual segment in a first region. The computing device is configured to apply the segment-specific transformation to an individual segment in a second region. The computing device is configured to retrieve a plurality of discrete magnification levels from a user. The computing device is configured to choose as a first magnification level from the plurality of discrete magnification levels, and the computing device is configured to store the plurality of segmentations in a cache.