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Related Concept Videos

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Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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GrandQC: A comprehensive solution to quality control problem in digital pathology.

Zhilong Weng1, Alexander Seper2, Alexey Pryalukhin3

  • 1Institute of Pathology, University Hospital Cologne, 50937, Cologne, Germany.

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|December 16, 2024
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Summary

GrandQC is a new tool for segmenting tissue and artifacts in histological slides, improving digital pathology image analysis. It establishes a quality control benchmark and is open-sourced to enhance sample preparation and scanning quality.

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Area of Science:

  • Digital Pathology
  • Computational Pathology
  • Medical Image Analysis

Background:

  • Histological slide artifacts significantly degrade digital pathology image analysis algorithm performance.
  • Quality control (QC) is crucial for reliable computational pathology but lacks standardized tools.

Purpose of the Study:

  • To develop and validate GrandQC, a tool for high-precision tissue and artifact segmentation in histological slides.
  • To establish a comprehensive QC benchmark using diverse international datasets.
  • To improve the performance of downstream image analysis algorithms in digital pathology.

Main Methods:

  • Development of the GrandQC tool for tissue and multi-class artifact segmentation.
  • Validation using slides from 19 international pathology departments and The Cancer Genome Atlas (TCGA) dataset.
  • Analysis of inter-institutional, intra-institutional, temporal, and inter-scanner slide quality variations.

Main Results:

  • GrandQC achieved high-precision tissue segmentation (Dice score 0.957).
  • Segmentation of artifact-free tissue yielded high Dice scores (0.919-0.938).
  • Established a QC benchmark, revealing variations in slide quality across institutions and scanners.

Conclusions:

  • GrandQC effectively segments tissue and artifacts, enhancing digital pathology image analysis.
  • The open-sourced tool, dataset, and TCGA QC masks address critical QC needs in computational pathology.
  • GrandQC serves as a vital tool for monitoring and improving sample preparation and scanning quality.