Whole Slide Image AI Quality Check for Pathology Artifacts
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Solution Overview
Problem
Existing digital pathology systems face challenges in efficiently detecting and correcting quality artifacts in whole slide images, such as misplaced tissue, air bubbles, and scanning errors, which can lead to inaccurate diagnoses and resource inefficiencies.
Innovation Solution
An automated quality control system using machine learning algorithms, including neural networks, to analyze thumbnail images derived from whole slide images, detects and classifies various artifacts, and triggers rescan if necessary, providing objective and efficient quality control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual quality control is performed by pathologists, then diagnostic accuracy can be maintained, but workflow efficiency decreases and human subjectivity increases
Solution Approach 1:
The system enables automated self-service quality control where the digital pathology system automatically detects and classifies artifacts without human intervention. The electronic neural network analyzes whole slide images to identify artifacts such as air bubbles, tissue folds, and staining issues, providing objective quality assessment that improves workflow efficiency while maintaining consistent reliability through automated decision-making
Solution Approach 2:
The patent replaces the mechanical system of manual visual inspection by pathologists with an electronic neural network-based automated detection system. This substitution eliminates human subjectivity and increases workflow efficiency by automatically analyzing images for artifacts, while the systematic approach ensures consistent quality control reliability across all samples
2Productivity
If automated artifact detection is implemented, then workflow efficiency improves, but system complexity increases
Solution Approach 1:
The system segments the quality control process into distinct functional modules: an electronic neural network for artifact detection, a classification module for categorizing artifacts by type and severity, and an integration layer that connects to the digital pathology system. This segmentation manages system complexity by organizing functions into manageable components while maintaining high quality control speed through specialized processing in each module
Solution Approach 2:
The electronic neural network is designed with multi-functionality to detect and classify multiple types of artifacts (air bubbles, tissue folds, staining issues, foreign objects) within a single integrated system. This universality improves workflow efficiency by handling diverse quality control tasks through one system rather than requiring separate specialized systems for each artifact type
3Reliability
If comprehensive artifact detection is performed, then diagnostic accuracy improves, but processing time increases
Solution Approach 1:
The automated electronic neural network system replaces time-consuming manual inspection while simultaneously improving diagnostic accuracy through comprehensive artifact detection. The system analyzes multiple artifact types simultaneously and provides objective classifications that enhance diagnostic reliability without the time penalty of manual review, as automated processing occurs in parallel and can be integrated into existing workflows
Data Source
AI summary
Techniques of automated quality control for digital pathology whole slide images are presented. The techniques include obtaining a thumbnail image derived from a whole slide image of a pathology slide; determining whether the whole slide image includes an artifact in a first class of artifacts by providing the thumbnail image to an electronic neural network trained to detect artifacts in the first class of artifacts by analyzing a plurality of labeled training thumbnail images; generating a tissue mask representing tissue depicted in the thumbnail image; determining whether the whole slide image includes an artifact in a second class of artifacts by performing a comparison using the tissue mask; and providing an indication of whether the whole slide image includes an artifact in the first class of artifacts or an artifact in the second class of artifacts.