The algorithm began with image upload and conversion from RGB to HSV color space. The images were uploaded into Matlab 2016b for further automated Ki-67 indexing. Cells were manually counted using ImageJ Win-64. Screenshots measuring approximately 1100x1700 pixels were downloaded for analysis. Images were digitally uploaded using an Omnyx VL120 Scanner and magnified to 40x. Ki-67 is represented by red and SOX-10 by brown nuclear staining in this assay. The algorithm was developed and implemented using Matlab 2016b.ĭesign: Ki-67/SOX-10 dual-stained slides high-grade melanomas were chosen for this study.
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ImageJ Windows 64-bit was used for manual cell counting. Technology: An Omnyx VL120 Scanner was used for digital slide scanning. In this study, we developed a custom computer-based algorithm to automatically calculate Ki-67 index in 3 Ki-67/SOX-10 dual-stained melanoma images with an emphasis on color-based image thresholding. Automated counting by image analyzers is the current gold-standard but has been shown to be expensive and impractical. “Eye-balling” is the least expensive and most widely used method but has poor reliability and reproducibility. However, adaptation into daily practice is met with challenges related to mode of assessment.
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Ĭontent: The Ki-67 labelling index has been shown to be a valuable prognostic indicator in various carcinomas including brain, breast, and skin tumors. The scores of the unsatisfied blocks sectioned with setting A improved significantly when sectioned with setting B. 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. It read sample information and printed barcode as well as input text and automatically generated slide order information. Results: The AS-410 provided auto-trimming function to detect exposed tissue for cutting, accomplished by the installed camera and calculation software. Auto-trimming and barcode reading and printing of AS-410 were also evaluated. And the scores from the two different settings were compared. Tissues with unsatisfied score were sectioned with modified setting (Setting B), and evaluated again by the same image scientist and pathologist with the same scoring systems.
Both scoring systems were scored from 1 to 5, with 1 the worst quality and 5 the highest quality. 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). 10 slides per block were sectioned and the last 5 slides were stained with H&E, digitized with WSI scanner, and evaluated by image scientist and pathologist. Nanozoomer 2.0HT (Hamamatsu, Japan) scanner was used to acquire the whole slide images (WSI) of the H&E stained slides at a resolution of 0.46 μm/pixel.ĭesign: Totally 77 surgical resection blocks of various organs embedded with standard paraffin were sectioned automatically using AS-410 at 5 μm with the default setting (Setting A). Ltd., Japan) which has the abilities of tissue detection, barcode reading and printing, and 3-8 μm tissue preparation, was used by this study. Technology: Tissue auto-sectioning machine AS-410 (Dainippon Seiki Co. In this study we were aimed to investigate a tissue automated sectioning machine for both clinical and research use. While tissue processing, 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. Content: Automation and digital pathology are the trends for future anatomic pathology with the increasing workload in histology laboratories.
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