Pathology Whole Slide Image Synthesis for Preanalytic Artifacts
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Solution Overview
Problem
Existing image processing systems in pathology face challenges in preanalytic quality control due to variations in slide preparation and scanning, leading to artifacts like bubbles, scan lines, and blur, which current methods struggle to simulate effectively, resulting in inconsistent performance across different laboratories.
Innovation Solution
Utilizing artificial intelligence (AI) to synthetically adjust scanned slide images by introducing or modifying artifacts such as air bubbles, thick tissue sections, and staining issues, creating synthetic whole slide images that mimic real-world preanalytic variations, thereby enhancing the robustness of AI systems to handle diverse slide preparations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional image processing methods are used to correct slide quality issues, then processing speed is maintained, but the ability to handle diverse preanalytic variations is insufficient
Solution Approach 1:
The system performs preliminary actions by synthetically introducing artifacts and quality issues into training images before AI model training. This pre-exposure to various preanalytic variations (staining differences, slide thickness variations, artifacts) enables the AI system to learn robust feature extraction and maintain consistent performance across diverse laboratory conditions without requiring actual re-scanning or re-preparation of slides.
Solution Approach 2:
The system creates synthetic copies of pathology slides with digitally introduced quality issues and artifacts. Instead of working with multiple physical slide copies that would require re-scanning, the invention generates digital replicas with simulated preanalytic variations, enabling comprehensive training data creation from a single source image while maintaining scalability and consistency.
2Manufacturing precision
If slides are reprepared or rescanned to correct quality issues, then image quality is improved, but time and resource efficiency deteriorates
Solution Approach 1:
The system replaces mechanical re-preparation or re-scanning processes with computational image processing and synthetic data generation. Instead of physically re-preparing slides or re-scanning them to correct quality issues, the invention uses AI-based synthetic image generation to create training data that accounts for quality variations, eliminating time-consuming physical operations while maintaining training effectiveness.
3Reliability
If AI systems are trained on diverse real-world slides with quality issues, then robustness improves, but data collection complexity increases
Solution Approach 1:
The system creates synthetic copies of pathology slides with digitally introduced quality issues and artifacts. Instead of working with multiple physical slide copies that would require re-scanning, the invention generates digital replicas with simulated preanalytic variations, enabling comprehensive training data creation from a single source image while maintaining scalability and consistency.
Solution Approach 2:
The system varies parameters of synthetic image generation to create diverse training data. By adjusting parameters such as artifact types, staining variations, slide thickness, and quality degradation levels, the system generates a wide range of training scenarios from a single source image, reducing the need to collect and manage complex multi-source real-world data while maintaining robustness training.
Data Source
AI summary
A method for processing electronic medical images may include receiving an initial whole slide image of a pathology specimen, receiving information about slide quality aspects to modify, and generating a synthetic whole slide image by applying a machine learning model to modify the received initial whole slide image according to the received information. The pathology specimen may be associated with a patient. The synthetic whole slide image may have a reduced quality as compared to the initial whole slide image.