A relationship between slide quality and image quality in whole slide imaging (WSI)

This study examined the effect of tissue section thickness and consistency – parameters outside the direct control of the imaging devices themselves – on WSI capture speed and image quality. Preliminary data indicates that thinner, more consistent tissue sectioning (such as those produced by automated tissue sectioning robots) result in significantly faster WSI capture times and better image quality

The importance of optical optimization in whole slide imaging (WSI) and digital pathology imaging

In the last 10 years, whole slide imaging (WSI) has seen impressive progress not only in image quality and scanning speed but also in the variety of systems available to pathologists. However, we have noticed that most systems have relatively simple optics axes and rely on software to optimize image quality and colour balance. While much can be done in software, this study examines the importance of optics, in particular optical filters, in WSI

Balancing Image Quality and Compression Factor for Special Stains Whole Slide Images

The objective is to find a practical balance between quality and performance for daily high volume whole slide imaging. We evaluated whole slide images created by various scanners at different compression factors to determine the best suitable quality factor (QF) needed for pathological images of special stains. Method: We scanned two sets of eight special stains slides each at 0.50 μm/pixel resolution in Hamamatsu scanner at six and five QF levels respectively to generate 72 images which were observed at a calibrated monitor by imaging specialists, a histo-technician, and a pathologist to find the most suitable QF level for special stains in digital slides. Results: Most special stains images were acceptable at QF 30 except for the stain Reticulin where the lowest acceptable QF was 50. The compression of images from QF 90 to QF 50 reduced the size of the images by 62.73%. Conclusion: 0.50 μm/pixel images at QF 50 or above were found suitable 12 special stain

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.    Lung Breast Prostat

Localization of Eosinophilic Esophagitis from H&E stained images using multispectral imaging

This study is an initial investigation on the capability of multispectral imaging to capture subtle spectral information that would enable the automatic delineation between the eosinophilic esophagitis and other eosin stained tissue components, especially the RBCs. In the method, a principal component analysis (PCA) was performed on the spectral transmittance samples of the different tissue components, excluding however the transmittance samples of the eosinophilic esophagitis. From the average spectral error configuration of the eosinophilic esophagitis transmittance samples, i.e. the difference between the actual transmittance and the estimated transmittance using m PC vectors, we indentified two spectral bands by which we can localize the eosinophils. Initial results show the possibility of automatically localizing the eosinophilic esophagitis by utilizing spectral information

Banff Digital Pathology Working Group: Going digital in transplant pathology.

The Banff Digital Pathology Working Group (DPWG) was formed in the time leading up to and during the joint American Society for Histocompatibility and Immunogenetics/Banff Meeting, September 23-27, 2019, held in Pittsburgh, Pennsylvania. At the meeting, the 14th Banff Conference, presentations directly and peripherally related to the topic of "digital pathology" were presented; and discussions before, during, and after the meeting have resulted in a list of issues to address for the DPWG. Included are practice standardization, integrative approaches for study classification, scoring of histologic parameters (eg, interstitial fibrosis and tubular atrophy and inflammation), algorithm classification, and precision diagnosis (eg, molecular pathways and therapeutics). Since the meeting, a survey with international participation of mostly pathologists (81%) was conducted, showing that whole slide imaging is available at the majority of centers (71%) but that artificial intelligence (AI)/machine learning was only used in ≈12% of centers, with a wide variety of programs/algorithms employed. Digitalization is not just an end in itself. It also is a necessary precondition for AI and other approaches. Discussions at the meeting and the survey highlight the unmet need for a Banff DPWG and point the way toward future contributions that can be made