Digital slide scanning at scale: Comparison of whole slide imaging devices in a clinical setting
Bibliographic record
Abstract
Background: Digital pathology requires additional resources such as specialized whole slide imaging systems, staffing, space, and information technology infrastructure. Optimization of slide scanner throughput and quality are critical to achieve proper digital scanning operations. However, vendor supplied scanner throughput and scan speeds are often cited for a theoretical 15 × 15 mm tissue area and do not capture the real-world complexities of pathology slides or clinical workflows that contribute to the total time to scan a glass slide (e.g., scanner operator time). This study compares real-world scanner throughput using clinically generated glass slides, evaluating image quality errors, and total true scan time for seven different vendors' commercially available high-throughput scanners. Design: Glass slides generated in a tertiary care CLIA-certified lab were retrieved from the departmental slide library including biopsies, surgical resections, and departmental consultation material from all surgical pathology subspecialties. Glass slide stain types include hematoxylin and eosin, immunohistochemical stains, or special stains per routine lab protocols. Slides were sequentially scanned by digital scan technicians on 16 different whole slide scanners from 7 different hardware vendor manufacturers. Two senior digital scan technicians reviewed each digital image that was generated from this study. One pathologist reviewed the set of slides for missing tissue determination. Scan times including scanner scan time, and time dedicated for pre- and post-scan work were recorded and summarized for the slide set for each scanner. Whole slide scanner models used in this study included: Leica Aperio AT2 and GT450 (Leica Biosystems, Buffalo Grove, Illinois); 3DHistech Pannoramic 1000, Philips UFS (Philips, Amsterdam, the Netherlands); Hamamatsu NanoZoomer S360 (Hamamatsu, Japan), Hologic Genius (Marlborough, MA), Huron TissueScope iQ (St. Jacobs Ontario, Canada) and 2-head Pramana Spectral HT scanning system (Pramana, Inc., Cambridge MA). Scanning was performed at ×40 equivalent magnification (∼0.25 μm per pixel) on each device, except for the Aperio AT2 and Huron TissueScope iQ which was ×20 equivalent magnification (0.5 μm per pixel). All scanner data were anonymized to guarantee unbiased interpretation of the results. Results: 347 glass slides representing real-world daily cases were assembled as a standardized slide set that was sequentially scanned on each device in this study. Variation in scan times for both the scanner model and labor time required to operate the scanner device were recorded. Actual instrument run time (e.g., scanner time) ranged between 7:30 and 43:02 (hours:minutes), the dedicated technician scanner operation time ranged from 1:30 to 9:24 h, and the total run time for each set, including the technician's time ranged from 13:30 to 47:02 h. Manual quality control review of the digital images detected quality errors in 8%-61% of the digital slides per run. Digital artifacts were recorded per scanner including missing tissue errors (0%-21%), out of focus errors (blur) (0%-30.1%), barcode failures (0%-26.2%), and tiling or overexposure were also documented in two scanners. Conclusion: Whole slide scanners which are manufactured by multiple vendors differ in their technical features which in turn affect scan time and image quality. High-throughput scanners are preferred for most high-volume clinical operations, yet their throughput and image quality varies among systems. Collection of this data is essential for assessing institutional resources and planning digital pathology use cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".