Evaluation of an Automated Tissue Sectioning Machine for Digital Pathology
Background: Automation and digital pathology are the trends for future anatomic pathology with the increasing workload in histology laboratories. While tissue process, embedding, staining and coverslipping, and digitizing have been available for automated use, tissue sectioning appears to be the biggest roadblock to a fully automated histology process. In this study we were aimed to investigate a tissue automated sectioning machine for both clinical and research use.
Methods: Totally 77 surgical resection blocks of various organs embedded with clinical standard paraffin were sectioned automatically using AS-410 (Dainippon Seiki Co. LTD., Japan) at 5 μm thickness with the default setting (Setting A). 10 slides per block were sectioned and the last 5 slides were stained with H&E. All stained slides were digitized with whole-slide imaging scanner, and then evaluated by the image scientist and the pathologist. The image scientist scored the images base on the extent of imperfection (Evaluation I), while the pathologist scored the images based on the clinical diagnosis purpose (Evaluation II). Both scoring systems were scored from 1 to 5, with 1 the worst quality and 5 the highest quality. Tissues with unsatisfied score were sectioned with modified setting (Setting B), and evaluated again by the same image scientist and pathologist using the same scoring systems. And the scores from the two different settings were compared. Auto-trimming and barcode reading and printing of AS-410 were also evaluated.
Results: The AS-410 provided auto-trimming function to detect exposed tissue for cutting, accomplished by the installed camera and calculation software. It read sample information and printed barcode as well as input text and automatically generated slide order information. It produced good quality of sections for most cases with median score more than 4 in both Evaluation I and Evaluation II using setting A. The scores of the unsatisfied blocks sectioned with setting A improved significantly when those blocks were sectioned with setting B.
Conclusion: The AS-410 tissue sectioning machine produces high-quality sections with clinical standard paraffin tissue blocks of a variety of organs with proper settings. It promises high automation with sound sectioning quality in the era of digital pathology for both clinical and research use.
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