Automated Quality Control for Digital Histology Micrographs
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Solution Overview
Problem
Current methods for analyzing digital histology slides are prone to gross errors and blurriness, which can be time-consuming and difficult to identify, necessitating automated assistance for quality control.
Innovation Solution
A two-stage quality review process using machine learning models, including deep convolutional neural networks, to identify quality failure cases and blur issues in digital micrographs of histology slides, generating a quality control report.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual quality review by technician is performed, then quality control can be conducted, but it is time-consuming and prone to human error
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated machine learning-based image analysis system. Multiple ML models process digital micrographs to detect quality issues such as blurriness, tissue folds, and staining problems, eliminating human involvement in the actual quality assessment while maintaining or improving accuracy.
Solution Approach 2:
The quality control system performs self-assessment by automatically analyzing its own input images through multiple machine learning models. The system independently identifies quality failures and generates reports without requiring external human intervention, enabling the system to serve its own quality control needs.
2Productivity
If automated machine learning models are applied, then quality review efficiency is improved, but device complexity increases
Solution Approach 1:
The patent divides the quality review task into multiple independent machine learning models, each specialized in detecting specific quality issues (e.g., blurriness, tissue folds, staining quality). This segmentation allows parallel processing of different quality aspects, increasing throughput while managing complexity through modular architecture.
Solution Approach 2:
The machine learning system is designed as a universal quality review platform that can handle multiple types of quality issues across different histology slide preparations. The same infrastructure supports various ML models that detect different defects, making the system multi-functional rather than requiring separate systems for each quality parameter.
3Measurement precision
If multiple machine learning models are used for comprehensive quality review, then quality detection accuracy is improved, but computational resources required increase
Solution Approach 1:
The patent segments the quality review into multiple specialized ML models that can process different aspects of image quality independently. This allows selective application of computational resources to specific quality parameters, improving detection accuracy for each parameter while optimizing overall resource usage through parallelized processing.
Solution Approach 2:
The system applies multiple machine learning models, which may represent an excessive approach compared to a single model, but this redundancy ensures comprehensive quality coverage. The partial application of different models to different quality aspects achieves high precision without requiring all models to process every image at maximum intensity.
Data Source
AI summary
Provided herein are methods and systems for performing an automated quality control analysis of digital micrographs representing slides with tissue samples. An automated quality control analysis may comprise analyzing digital micrographs of histology slides for gross errors and excessive regions of blurriness.