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Measuring Digital Pathology Throughput and Tissue Dropouts.

George L Mutter1,1, David S Milstone1,1, David H Hwang1,1

  • 1Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.

Journal of Pathology Informatics
|February 9, 2022
PubMed
Summary

Digital pathology scanner performance varies significantly in speed, file size, and image completeness. Optimized settings and scanner choice are crucial for efficient and accurate digital slide analysis.

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Area of Science:

  • Digital Pathology
  • Medical Imaging
  • Computational Pathology

Background:

  • Digital pathology workflows significantly impact operational costs and image fidelity.
  • Scan time, file size, and tissue dropouts are key variables affecting throughput, storage, and diagnostic accuracy.

Purpose of the Study:

  • To compare scan time, file size, and image completeness across different digital pathology slide scanners.
  • To evaluate the impact of scanner type and operational settings on digital tissue dropout rates.

Main Methods:

  • A set of 212 gynecologic-gestational pathology slides was used for benchmarking.
  • Scanners benchmarked included Hamamatsu S210 (default and optimized profiles) and Leica GT450.
  • Tissue dropouts were detected by aligning whole slide images with macroscopic reference images.
Keywords:
Digital pathologydropoutsimage analysisoperationsscannerwhole-slide imaging

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Main Results:

  • Scan time and file size varied greatly between scanners and settings (e.g., 93s/1.4GB for optimized S210 vs. 721s/3.4GB for default S210).
  • Tissue dropouts occurred in 29.5% of scans, with the optimized S210 profile showing the lowest rate (13.7%).
  • The majority of dropouts were minor (shards, marginal tissue) or contaminants, rarely affecting diagnostic content.

Conclusions:

  • Scanner type, operational settings, and specimen characteristics significantly influence scanning speed, file size, and image fidelity.
  • Digital pathology platforms exhibit variable output efficiency and fidelity, necessitating selection based on specific applications.
  • While dropouts occur, they infrequently represent actual diagnostic tissue, suggesting minimal impact on diagnoses in most cases.