Automated Digital Pathology Slide Quality Control
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Solution Overview
Problem
Existing digital pathology (DP) quality control processes are manual, subjective, labor-intensive, slow, and error-prone, leading to variability in slide presentation and quality due to artifacts and batch effects, which can impact clinical and research applications.
Innovation Solution
An automated digital pathology slide quality control system that employs a combination of image metrics, features, and supervised classifiers to identify and correct for presentation differences, including artifacts and batch effects, using a modular approach that can be integrated with existing workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review of glass and digital slides is performed, then quality control can be conducted, but the process is laborious, subjective, and subject to inter-reviewer and intra-reviewer variability
Solution Approach 1:
The patent replaces manual mechanical review processes with automated computational image analysis systems. Machine learning algorithms and computer vision techniques are used to objectively assess slide quality, eliminating inter-reviewer and intra-reviewer variability while maintaining high reliability in quality control assessments.
Solution Approach 2:
The system enables self-service quality control by allowing the digital pathology workflow to automatically assess and flag artifacts without requiring manual intervention. The automated pipeline performs quality assessment as an integral part of the workflow, reducing labor burden while maintaining consistent quality standards.
2Productivity
If manual quality control processes are used, then slides can be reviewed, but the process is slow and labor-intensive
Solution Approach 1:
The patent replaces time-consuming manual review processes with automated computational systems that can process multiple slides simultaneously. The automated image analysis pipeline performs quality assessment in minutes rather than hours, dramatically increasing throughput while reducing time loss.
Solution Approach 2:
The system implements continuous quality control as an integrated part of the digital pathology workflow rather than as a separate batch process. Quality assessment occurs automatically during slide processing, eliminating idle time and ensuring continuous productive action throughout the workflow.
3Extent of automation
If automated quality control is implemented, then productivity and consistency are improved, but system complexity increases
Solution Approach 1:
The patent segments the automated quality control system into modular functional components: image preprocessing modules, artifact detection algorithms, quality scoring systems, and reporting mechanisms. This segmentation allows each component to be independently optimized and maintained, reducing overall system complexity while maintaining high automation levels.
Solution Approach 2:
The system implements universal quality control algorithms that can assess multiple types of artifacts (folding, tearing, staining variations, imaging artifacts) using a single integrated platform. This multi-functionality reduces the need for separate specialized systems, managing complexity while providing comprehensive automation.
Data Source
AI summary
Embodiments include accessing a set of digital pathology (DP) images having an imaging parameter; applying a low-computational cost histology quality control (HistoQC) pipeline to the DP images, where the low-computational cost HistoQC pipeline computes a first set of image metrics associated with a DP image, and assigns the DP image to a first or a second, different cohort based on the imaging parameter and the first set of image metrics; applying a first, higher-computational-cost HistoQC pipeline to a member of the first cohort; applying a second, different higher-computation-cost HistoQC pipeline to a member of the second cohort; where the first or second, higher-computational-cost HistoQC pipeline determines an artifact-free region of the member of the first or second cohort, respectively, and classifies the member of the first or second cohort, respectively, as suitable or unsuitable for downstream computation or diagnostic analysis based, at least in part, on the artifact free region.