Slide Imaging Scanners Research Articles

Diagnosis of odontogenic keratocysts and non-keratocysts using edge attention convolution neural network.

The study's objective was to develop an automated method for a histopathology recognition model for odontogenic keratocysts (OKC) and non-keratocyst (Non-KC) in jaw cyst sections stained with hematoxylin (H) and eosin (E) on a tiny bit of incisional biopsy prior to surgery. This hastens the speed and precision of diagnosis to patients. Also, navigates the clinicians with the therapeutic doctrine. To build such a system and to increase the accuracy of the existing models, the edge attention CNN model with Keras functional API was implemented which efficiently analyzes the texture information of the images. Approximately 2861 microscopic images at a 40X magnification were taken from 54 OKC, 23 Dentigerous cysts (DC), and 20 Radicular cysts. The model was trained using both RGB and canny edge-detected images. The model gave a good accuracy of 96.8%, which is suitable for real-time. Histopathological images are better analyzed through textural features. The proposed edge attention CNN highlights the edges, making texture analysis more precise. The suggested method will work for OKC and Non-KC diagnosis automation systems. The use of a whole slide imaging scanner has the potential to increase accuracy and remove human bias.

Morphometric Analysis of Lateral Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma Using Digital Pathology.

Digital pathology uses digitized images for cancer research. We aimed to assess morphometric parameters using digital pathology for predicting recurrence in patients with papillary thyroid carcinoma (PTC) and lateral cervical lymph node (LN) metastasis. We analyzed 316 PTC patients and assessed the longest diameter and largest area of metastatic focus in LNs using a whole slide imaging scanner. In digital pathology assessment, the longest diameters and largest areas of metastatic foci in LNs were positively correlated with traditional optically measured diameters (R = 0.928 and R2 = 0.727, p < 0.001 and p < 0.001, respectively). The optimal cutoff diameter was 8.0mm in both traditional microscopic (p = 0.009) and digital pathology (p = 0.016) evaluations, with significant differences in progression-free survival (PFS) observed at this cutoff (p = 0.006 and p = 0.002, respectively). The predictive area's cutoff was 35.6 mm2 (p = 0.005), which significantly affected PFS (p = 0.015). Using an 8.0-mm cutoff in traditional microscopic evaluation and a 35.6-mm2 cutoff in digital pathology showed comparable predictive results using the proportion of variation explained (PVE) methods (2.6% vs. 2.4%). Excluding cases with predominant cystic changes in LNs, the largest metastatic areas by digital pathology had the highest PVE at 3.9%. Furthermore, high volume of LN metastasis (p = 0.001), extranodal extension (p = 0.047), and high ratio of metastatic LNs (p = 0.006) were associated with poor prognosis. Both traditional microscopic and digital pathology evaluations effectively measured the longest diameter of metastatic foci in LNs. Moreover, digital pathology offers limited advantages in predicting PFS of patients with lateral cervical LN metastasis of PTC, especially those without predominant cystic changes in LNs.

Assessment of Color Reproducibility and Mitigation of Color Variation in Whole Slide Image Scanners for Toxicologic Pathology.

Digital pathology workflows in toxicologic pathology rely on whole slide images (WSIs) from histopathology slides. Inconsistent color reproduction by WSI scanners of different models and from different manufacturers can result in different color representations and inter-scanner color variation in the WSIs. Although pathologists can accommodate a range of color variation during their evaluation of WSIs, color variability can degrade the performance of computational applications in digital pathology. In particular, color variability can compromise the generalization of artificial intelligence applications to large volumes of data from diverse sources. To address these challenges, we developed a process that includes two modules: (1) assessing the color reproducibility of our scanners and the color variation among them and (2) applying color correction to WSIs to minimize the color deviation and variation. Our process ensures consistent color reproduction across WSI scanners and enhances color homogeneity in WSIs, and its flexibility enables easy integration as a post-processing step following scanning by WSI scanners of different models and from different manufacturers.

A comprehensive multi-domain dataset for mitotic figure detection

The prognostic value of mitotic figures in tumor tissue is well-established for many tumor types and automating this task is of high research interest. However, especially deep learning-based methods face performance deterioration in the presence of domain shifts, which may arise from different tumor types, slide preparation and digitization devices. We introduce the MIDOG++ dataset, an extension of the MIDOG 2021 and 2022 challenge datasets. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology with in total labels for 11,937 mitotic figures: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The specimens were processed in several laboratories utilizing diverse scanners. We evaluated the extent of the domain shift by using state-of-the-art approaches, observing notable differences in single-domain training. In a leave-one-domain-out setting, generalizability improved considerably. This mitotic figure dataset is the first that incorporates a wide domain shift based on different tumor types, laboratories, whole slide image scanners, and species.

Diagnostic validation of a portable whole slide imaging scanner for lymphoma diagnosis in resource-constrained setting: A cross-sectional study

BackgroundTelepathology utilizing high-throughput static whole slide image scanners is proposed to address the challenge of limited pathology services in resource-restricted settings. However, the prohibitive equipment costs and sophisticated technologies coupled with large amounts of space to set up the devices make it impractical for use in resource-limited settings. Herein, we aimed to address this challenge by validating a portable whole slide imaging (WSI) device against glass slide microscopy (GSM) using lymph node biopsies from suspected lymphoma cases from Sub-Saharan Africa. Material and methodsThis was part of a multicenter prospective case–control head-to-head comparison study of liquid biopsy against conventional pathology. For the portable WSI scanner validation, the study pathologists evaluated 105 surgical lymph node specimens initially confirmed by gold-standard pathology between February and December 2021. The tissues were processed according to standard protocols for Hematoxylin and Eosin (H&E) and Immunohistochemistry (IHC) staining by well-trained histotechnicians, then digitalized the H& E and IHC slides at each center. The digital images were anonymized and uploaded to a HIPAA-compliant server by the histotechnicians. Three study pathologists independently accessed and reviewed the images after a 6-week washout. The agreement between diagnoses established on GSM and WSI across the pathologists was described and measured using Cohens’ kappa coefficient (κ). ResultsOn GSM, 65.5% (n=84) of specimens were lymphoma; 25% were classified as benign, while 9.5% were metastatic. Morphological quality assessment on GSM and WSI established that 79.8% and 53.6% of cases were of high quality, respectively. When diagnoses by GSM were compared to WSI, the overall concordance for various diagnostic categories was 93%, 100%, and 86% for lymphoma, metastases, and benign conditions respectively. The sensitivity and specificity of WSI for the detection of lymphoma were 95.2% and 85.7%, respectively, with an overall inter-observer agreement (κ) of 0.86; 95% CI (0.70–0.95). ConclusionsWe demonstrate that mobile whole slide imaging (WSI) is non-inferior to conventional glass slide microscopy (GSM) for the primary diagnosis of malignant infiltration of lymph node specimens. Our results further provide proof of concept that mobile WSI can be adapted to resource-restricted settings for primary surgical pathology and would significantly improve patient outcomes.

Real-Time, Automated, Multiobjective, Cloud Computing Whole Slide Imaging Device

We present a novel approach to Whole slide imaging that consists of a uniquely designed, Low-cost, single-unit-portable, optically isolated-dust free, onboard mini-computer integrated, fully enclosed Whole Slide Imaging scanner device that can be wirelessly controlled through an easy-to-use, intuitive user interface-application on an iPad. A LabVIEW-based control software interfaces with multiple hardware modules within the device using the Queued Message Handler (QMH) architecture optimized to handle synchronous and parallel real-time processes. The system scans an area of 15×15 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in ~5 minutes at a resolution of ~0.25μm with a 40x Objective Lens. With a low weight of 12kg, the enclosure dimensions being 340 × 265 × 300 mm, and its low cost of building the device makes it more portable and affordable to the intended rural and low-tier medical centers. The instrument incorporates features such as automatic slide loading, slide registration, Brightness control, Slide scanning, and an Autofocus algorithm implementing a unique “convolution-histogram mean value” method to determine the Z-focus map. The work showcases a shift in pre-processing computations to the cloud server and integrating AI/ML tools for advanced database and image processing techniques on a portable device. A non-medical application of studying plasma science using the instrument has also been presented, demonstrating its wider application in medical and non-medical fields.

On Smart Gaze Based Annotation of Histopathology Images for Training of Deep Convolutional Neural Networks.

Unavailability of large training datasets is a bottleneck that needs to be overcome to realize the true potential of deep learning in histopathology applications. Although slide digitization via whole slide imaging scanners has increased the speed of data acquisition, labeling of virtual slides requires a substantial time investment from pathologists. Eye gaze annotations have the potential to speed up the slide labeling process. This work explores the viability and timing comparisons of eye gaze labeling compared to conventional manual labeling for training object detectors. Challenges associated with gaze based labeling and methods to refine the coarse data annotations for subsequent object detection are also discussed. Results demonstrate that gaze tracking based labeling can save valuable pathologist time and delivers good performance when employed for training a deep object detector. Using the task of localization of Keratin Pearls in cases of oral squamous cell carcinoma as a test case, we compare the performance gap between deep object detectors trained using hand-labelled and gaze-labelled data. On average, compared to 'Bounding-box' based hand-labeling, gaze-labeling required 57.6% less time per label and compared to 'Freehand' labeling, gaze-labeling required on average 85% less time per label.

Affordable artificial intelligence-based digital pathology for neglected tropical diseases: A proof-of-concept for the detection of soil-transmitted helminths and Schistosoma mansoni eggs in Kato-Katz stool thick smears.

BackgroundWith the World Health Organization’s (WHO) publication of the 2021–2030 neglected tropical diseases (NTDs) roadmap, the current gap in global diagnostics became painfully apparent. Improving existing diagnostic standards with state-of-the-art technology and artificial intelligence has the potential to close this gap.Methodology/Principal findingsWe prototyped an artificial intelligence-based digital pathology (AI-DP) device to explore automated scanning and detection of helminth eggs in stool prepared with the Kato-Katz (KK) technique, the current diagnostic standard for diagnosing soil-transmitted helminths (STHs; Ascaris lumbricoides, Trichuris trichiura and hookworms) and Schistosoma mansoni (SCH) infections. First, we embedded a prototype whole slide imaging scanner into field studies in Cambodia, Ethiopia, Kenya and Tanzania. With the scanner, over 300 KK stool thick smears were scanned, resulting in total of 7,780 field-of-view (FOV) images containing 16,990 annotated helminth eggs (Ascaris: 8,600; Trichuris: 4,083; hookworms: 3,623; SCH: 684). Around 90% of the annotated eggs were used to train a deep learning-based object detection model. From an unseen test set of 752 FOV images containing 1,671 manually verified STH and SCH eggs (the remaining 10% of annotated eggs), our trained object detection model extracted and classified helminth eggs from co-infected FOV images in KK stool thick smears, achieving a weighted average precision (± standard deviation) of 94.9% ± 0.8% and a weighted average recall of 96.1% ± 2.1% across all four helminth egg species.Conclusions/SignificanceWe present a proof-of-concept for an AI-DP device for automated scanning and detection of helminth eggs in KK stool thick smears. We identified obstacles that need to be addressed before the diagnostic performance can be evaluated against the target product profiles for both STH and SCH. Given that these obstacles are primarily associated with the required hardware and scanning methodology, opposed to the feasibility of AI-based results, we are hopeful that this research can support the 2030 NTDs road map and eventually other poverty-related diseases for which microscopy is the diagnostic standard.

Deep Learning-Based Microscopic Diagnosis of Odontogenic Keratocysts and Non-Keratocysts in Haematoxylin and Eosin-Stained Incisional Biopsies.

Background: The goal of the study was to create a histopathology image classification automation system that could identify odontogenic keratocysts in hematoxylin and eosin-stained jaw cyst sections. Methods: From 54 odontogenic keratocysts, 23 dentigerous cysts, and 20 radicular cysts, about 2657 microscopic pictures with 400× magnification were obtained. The images were annotated by a pathologist and categorized into epithelium, cystic lumen, and stroma of keratocysts and non-keratocysts. Preprocessing was performed in two steps; the first is data augmentation, as the Deep Learning techniques (DLT) improve their performance with increased data size. Secondly, the epithelial region was selected as the region of interest. Results: Four experiments were conducted using the DLT. In the first, a pre-trained VGG16 was employed to classify after-image augmentation. In the second, DenseNet-169 was implemented for image classification on the augmented images. In the third, DenseNet-169 was trained on the two-step preprocessed images. In the last experiment, two and three results were averaged to obtain an accuracy of 93% on OKC and non-OKC images. Conclusions: The proposed algorithm may fit into the automation system of OKC and non-OKC diagnosis. Utmost care was taken in the manual process of image acquisition (minimum 28–30 images/slide at 40× magnification covering the entire stretch of epithelium and stromal component). Further, there is scope to improve the accuracy rate and make it human bias free by using a whole slide imaging scanner for image acquisition from slides.

Colorimetrical uncertainty estimation for the performance assessment of whole slide imaging scanners.

Purpose: Whole slide imaging (WSI) scanners produce tissue slide images with a large field of view and a high resolution for pathologists to use in diagnoses. Color performance tests of these color imaging devices are necessary and can use stained tissue slides if the color truth is established using a hyperspectral imaging microscopy system (HIMS). The purpose of this study was to estimate the reproducibility uncertainty of CIELAB coordinates for a reference tissue slide measured by both the HIMS and a WSI scanner. Approach: We compared the color performances of the WSI scanner to those of the reference established by the HIMS using the International Commission on Illumination (Commission Internationale de l'Éclairage, or CIE) 1976 color difference with the just noticeable color difference (JNCD, ), and the results from the overlap of the CIELAB coordinates' uncertainty within the error bar, with a coverage factor . The reported uncertainty results from measurements and image registration uncertainties. Results: For the blank area common to the HIMS and the WSI average images, the color agreement was higher using the JNCD condition versus the CIELAB uncertainty overlap criterion (82% and 20% of the pixels in the images, respectively). This difference is explained by the fact that numerous pixels have CIELAB coordinates near one another but corresponding to CIELAB uncertainty values small enough not to overlap. In the colored area of the images, the JNCD condition was met for 0.19% of the pixels in the images, compared with 4.3% for the CIELAB uncertainty overlap criterion. Conclusions: The distribution of uncertainties on the CIELAB coordinates was broader for the HIMS compared with the WSI scanner. The WSI scanner had a systemic error in the color reproduction, which pointed to a potential inadequate color calibration of this device.

Abstract 5: Establishment of ASCP Equipment Donation Program to Support International Histopathology Laboratories

Abstract Purpose: In order to address the unmet needs of international histopathology laboratories with regard to procurement of laboratory equipment and consumables, the ASCP Center for Global Health has established a program which facilitates donation of these goods to laboratories in need. Methods: Using a member-based outreach approach, donors with key histopathology laboratory equipment or consumables submit an interest in donation to ASCP. ASCP performs an assessment of the quality and longevity of the donated item. ASCP pairs the donation with a laboratory within the Partners for Cancer Diagnosis and Treatment Initiative network which has expressed need. Recipient lab-specific parameters are further evaluated (e.g. space, voltage compatibility, need for service contract, etc.). Should the donation be a compatible fit, ASCP works with both recipient and international shippers to facilitate the donation. Results: Since August 2018, ASCP has supported nearly 20 international equipment donations (fair market value of goods totaling approximately $120K) to 12 countries. These donations are composed of routine histopathology lab consumables (e.g. tissue cassettes, glass slides, etc.) as well as specialized equipment (e.g. whole slide imaging scanners, microtomes, microscopes, etc.), basic laboratory operations equipment (e.g. computers, keyboards, etc), and educational materials (e.g. textbooks, etc.). Conclusions: This donation program allows for redistribution of key histopathology lab equipment and consumables to laboratories in needs. The impact of this program has been far-reaching, both relieving laboratory strain associated with under-procurement often experienced in international laboratories to expanding capacitation of diagnostic testing services including, in some cases, telepathology-based secondary consultations from subspecialty pathologists unavailable in-country. Citation Format: Debby Basu, Lola Tashpulatova, Dan Milner, Kenneth Landgraf. Establishment of ASCP Equipment Donation Program to Support International Histopathology Laboratories [abstract]. In: Proceedings of the 9th Annual Symposium on Global Cancer Research; Global Cancer Research and Control: Looking Back and Charting a Path Forward; 2021 Mar 10-11. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2021;30(7 Suppl):Abstract nr 5.

Automated 3D scoring of fluorescence in situ hybridization (FISH) using a confocal whole slide imaging scanner

Fluorescence in situ hybridization (FISH) is a technique to visualize specific DNA/RNA sequences within the cell nuclei and provide the presence, location and structural integrity of genes on chromosomes. A confocal Whole Slide Imaging (WSI) scanner technology has superior depth resolution compared to wide-field fluorescence imaging. Confocal WSI has the ability to perform serial optical sections with specimen imaging, which is critical for 3D tissue reconstruction for volumetric spatial analysis. The standard clinical manual scoring for FISH is labor-intensive, time-consuming and subjective. Application of multi-gene FISH analysis alongside 3D imaging, significantly increase the level of complexity required for an accurate 3D analysis. Therefore, the purpose of this study is to establish automated 3D FISH scoring for z-stack images from confocal WSI scanner. The algorithm and the application we developed, SHIMARIS PAFQ, successfully employs 3D calculations for clear individual cell nuclei segmentation, gene signals detection and distribution of break-apart probes signal patterns, including standard break-apart, and variant patterns due to truncation, and deletion, etc. The analysis was accurate and precise when compared with ground truth clinical manual counting and scoring reported in ten lymphoma and solid tumors cases. The algorithm and the application we developed, SHIMARIS PAFQ, is objective and more efficient than the conventional procedure. It enables the automated counting of more nuclei, precisely detecting additional abnormal signal variations in nuclei patterns and analyzes gigabyte multi-layer stacking imaging data of tissue samples from patients. Currently, we are developing a deep learning algorithm for automated tumor area detection to be integrated with SHIMARIS PAFQ.

Clinical deployment of AI for prostate cancer diagnosis

Clinical deployment of AI for prostate cancer diagnosis

Paper] Automatic Quality Evaluation of Whole Slide Images for the Practical Use of Whole Slide Imaging Scanner

Paper] Automatic Quality Evaluation of Whole Slide Images for the Practical Use of Whole Slide Imaging Scanner

Cancer Image Quantification With And Without, Expensive Whole Slide Imaging Scanners.

Automated analysis of digitized pathology images in tele-health applications can have a transformative impact on under-served communities in the developing world. However, the vast majority of existing image analysis algorithms are trained on slide images acquired via expensive Whole-Slide-Imaging (WSI) scanners. High scanner cost is a key bottleneck preventing large-scale adoption of digital pathology in developing countries. In this work, we investigate the viability of automated analysis of slide images captured from the eyepiece of a microscope via a smart phone. The mitosis detection application is considered as a use case.Results indicate performance degradation when using (lower-quality) smartphone images; as expected. However, the performance gap is not too wide (F1-score smartphone=0.65, F1-score WSI=0.70) demonstrating that smartphones could potentially be employed as image acquisition devices for digital pathology at locations where expensive scanners are not available.

Cover Information

Color Research & ApplicationVolume 44, Issue 3 p. i-i COVER INFORMATIONFree Access Cover Information First published: 01 April 2019 https://doi.org/10.1002/col.22377AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Graphical Abstract The cover image is based on the Research Article Assessing color performance of whole–slide imaging scanners for digital pathology by Wei–Chung Cheng et al., https://doi.org/10.1002/col.22365. Volume44, Issue3June 2019Pages i-i RelatedInformation

Digital Microscopy, Image Analysis, and Virtual Slide Repository.

Advancements in technology and digitization have ushered in novel ways of enhancing tissue-based research via digital microscopy and image analysis. Whole slide imaging scanners enable digitization of histology slides to be stored in virtual slide repositories and to be viewed via computers instead of microscopes. Easier and faster sharing of histologic images for teaching and consultation, improved storage and preservation of quality of stained slides, and annotation of features of interest in the digital slides are just a few of the advantages of this technology. Combined with the development of software for digital image analysis, digital slides further pave the way for the development of tools that extract quantitative data from tissue-based studies. This review introduces digital microscopy and pathology, and addresses technical and scientific considerations in slide scanning, quantitative image analysis, and slide repositories. It also highlights the current state of the technology and factors that need to be taken into account to insure optimal utility, including preanalytical considerations and the importance of involving a pathologist in all major steps along the digital microscopy and pathology workflow.

A quantitative approach to evaluate image quality of whole slide imaging scanners

Context: The quality of images produced by whole slide imaging (WSI) scanners has a direct influence on the readers’ performance and reliability of the clinical diagnosis. Therefore, WSI scanners should produce not only high quality but also consistent quality images. Aim: We aim to evaluate reproducibility of WSI scanners based on the quality of images produced over time and among multiple scanners. The evaluation is independent of content or context oftest specimen. Methods: The ultimate judge of image quality is a pathologist, however, subjective evaluations are heavily influenced by the complexity of a case and subtle variations introduced by a scanner can be easily overlooked. Therefore, we employed a quantitative image quality assessment method based on clinically relevant parameters, such as sharpness and brightness, acquired in a survey of pathologists. The acceptable level of quality per parameter was determined in a subjective study. The evaluation of scanner reproducibility was conducted with Philips Ultra-Fast Scanners. A set of 36 HercepTest™ slides were used in three sub-studies addressing variations due to systems and time, producing 8640 test images for evaluation. Results: The results showed that the majority of images in all the sub-studies are within the acceptable quality level; however, some scanners produce higher quality images more often than others. The results are independent of case types, and they match our perception of quality. Conclusion: The quantitative image quality assessment method was successfully applied in the HercepTest™ slides to evaluate WSI scanner reproducibility. The proposed method is generic and applicable to any other types of slide stains and scanners.

Validation of Multiple Whole Slide Imaging Scanners Based on the Guideline From the College of American Pathologists Pathology and Laboratory Quality Center

Whole slide imaging (WSI) produces a virtual image that can be transmitted electronically. This technology has clinical applications in situations in which glass slides are not readily available. To examine the results of a validation study performed using the draft version of the WSI clinical validation guideline recently released by the College of American Pathologists. Ten iScan Coreo Au scanners (Ventana Medical Systems, Tucson, Arizona) were validated, 6 with one set of 100 cases and 4 with a different set of 100 cases, for 1000 case examinations. The cases were selected consecutively from the following case types: internal consultations and malignancies and cases with frozen sections, special stains, and/or immunohistochemistry. Only key slides were scanned from each case. The slides were scanned at ×20 magnification. Pathologists reviewed the cases as both glass slides and WSI, with at least a 3-week washout period between viewings. Intraobserver agreement between glass slides and WSI was present for 786 (79%) of the 1000 cases. Major discrepancies occurred in 18 cases (1.8%). κ statistics compiled for the subset of cases (n = 504; 50%) with concern for neoplasia showed excellent agreement (κ = 0.8782). Individual scanners performed similarly to one another. Analysis of the results revealed an area of concern: small focal findings. The results were felt to validate the use of WSI for the intended applications in our multiinstitutional laboratory system, although scans at ×20 magnification may be insufficient for cases hinging on small focal findings, such as microorganisms and inflammatory processes.

Color accuracy and reproducibility in whole slide imaging scanners.

We propose a workflow for color reproduction in whole slide imaging (WSI) scanners, such that the colors in the scanned images match to the actual slide color and the inter-scanner variation is minimum. We describe a new method of preparation and verification of the color phantom slide, consisting of a standard IT8-target transmissive film, which is used in color calibrating and profiling the WSI scanner. We explore several International Color Consortium (ICC) compliant techniques in color calibration/profiling and rendering intents for translating the scanner specific colors to the standard display (sRGB) color space. Based on the quality of the color reproduction in histopathology slides, we propose the matrix-based calibration/profiling and absolute colorimetric rendering approach. The main advantage of the proposed workflow is that it is compliant to the ICC standard, applicable to color management systems in different platforms, and involves no external color measurement devices. We quantify color difference using the CIE-DeltaE2000 metric, where DeltaE values below 1 are considered imperceptible. Our evaluation on 14 phantom slides, manufactured according to the proposed method, shows an average inter-slide color difference below 1 DeltaE. The proposed workflow is implemented and evaluated in 35 WSI scanners developed at Philips, called the Ultra Fast Scanners (UFS). The color accuracy, measured as DeltaE between the scanner reproduced colors and the reference colorimetric values of the phantom patches, is improved on average to 3.5 DeltaE in calibrated scanners from 10 DeltaE in uncalibrated scanners. The average inter-scanner color difference is found to be 1.2 DeltaE. The improvement in color performance upon using the proposed method is apparent with the visual color quality of the tissue scans.