Metal-enhanced fluorescence (MEF) effect based on silver nanoparticles with different UV spectra on a surface carbon dot-based novel dry platform.

In this work, to enhance the fluorescence quantum yield of carbon dots (CDs), a novel metal-enhanced fluorescence (MEF) structure was designed by decorating CDs on silver nanoparticle (AgNPs) film. The glass slide-AgNPs (GS-AgNPs) structure was fabricated using the electrostatic adsorption method, and the AgNPs-CDs structures were prepared by the direct drying method, which then formed the GS-AgNPs-CDs composite structure. In this structure, the MEF effect was found to be size dependent by changing the 5 types of AgNPs with different sizes. And the MEF effect also decreased as the distance between the AgNPs and CDs increased by using polyvinylpyrrolidone (PVP) to separate the AgNPs and CDs. This hybrid structure can be used as a fluorescence detection platform and the recorded fluorescence intensity of GS-AgNPs 428 nm-CDs achieved a maximum enhancement factor (EF) of 31.72. Considering the high enhancement factor, this system may become promising to find potential applications in biochemical assay fields.

Integrating spatial transcriptomics and bulk RNA-seq: predicting gene expression with enhanced resolution through graph attention networks.

Spatial transcriptomics data play a crucial role in cancer research, providing a nuanced understanding of the spatial organization of gene expression within tumor tissues. Unraveling the spatial dynamics of gene expression can unveil key insights into tumor heterogeneity and aid in identifying potential therapeutic targets. However, in many large-scale cancer studies, spatial transcriptomics data are limited, with bulk RNA-seq and corresponding Whole Slide Image (WSI) data being more common (e.g. TCGA project). To address this gap, there is a critical need to develop methodologies that can estimate gene expression at near-cell (spot) level resolution from existing WSI and bulk RNA-seq data. This approach is essential for reanalyzing expansive cohort studies and uncovering novel biomarkers that have been overlooked in the initial assessments. In this study, we present STGAT (Spatial Transcriptomics Graph Attention Network), a novel approach leveraging Graph Attention Networks (GAT) to discern spatial dependencies among spots. Trained on spatial transcriptomics data, STGAT is designed to estimate gene expression profiles at spot-level resolution and predict whether each spot represents tumor or non-tumor tissue, especially in patient samples where only WSI and bulk RNA-seq data are available. Comprehensive tests on two breast cancer spatial transcriptomics datasets demonstrated that STGAT outperformed existing methods in accurately predicting gene expression. Further analyses using the TCGA breast cancer dataset revealed that gene expression estimated from tumor-only spots (predicted by STGAT) provides more accurate molecular signatures for breast cancer sub-type and tumor stage prediction, and also leading to improved patient survival and disease-free analysis. Availability: Code is available at https://github.com/compbiolabucf/STGAT.

The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue.

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results provide a comparison of the performance of current WSI registration methods and guide researchers in selecting and developing methods.

Development of a multi-scanner facility for data acquisition for digital pathology artificial intelligence.

Whole slide imaging (WSI) of pathology glass slides using high-resolution scanners has enabled the large-scale application of artificial intelligence (AI) in pathology, to support the detection and diagnosis of disease, potentially increasing efficiency and accuracy in tissue diagnosis. Despite the promise of AI, it has limitations. 'Brittleness' or sensitivity to variation in inputs necessitates that large amounts of data are used for training. AI is often trained on data from different scanners but not usually by replicating the same slide across scanners. The utilisation of multiple WSI instruments to produce digital replicas of the same slides will make more comprehensive datasets and may improve the robustness and generalisability of AI algorithms as well as reduce the overall data requirements of AI training. To this end, the National Pathology Imaging Cooperative (NPIC) has built the AI FORGE (Facilitating Opportunities for Robust Generalisable data Emulation), a unique multi-scanner facility embedded in a clinical site in the NHS to (1) compare scanner performance, (2) replicate digital pathology image datasets across WSI systems, and (3) support the evaluation of clinical AI algorithms. The NPIC AI FORGE currently comprises 15 scanners from nine manufacturers. It can generate approximately 4,000 WSI images per day (approximately 7 TB of image data). This paper describes the process followed to plan and build such a facility. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Roadmap for providing and leveraging annotated data by cytologists in the PDAC domain as open data: support for AI-based pathology image analysis development and data utilization strategies.

Pancreatic cancer is one of the most lethal cancers worldwide, with a 5-year survival rate of less than 5%, the lowest of all cancer types. Pancreatic ductal adenocarcinoma (PDAC) is the most common and aggressive pancreatic cancer and has been classified as a health emergency in the past few decades. The histopathological diagnosis and prognosis evaluation of PDAC is time-consuming, laborious, and challenging in current clinical practice conditions. Pathological artificial intelligence (AI) research has been actively conducted lately. However, accessing medical data is challenging; the amount of open pathology data is small, and the absence of open-annotation data drawn by medical staff makes it difficult to conduct pathology AI research. Here, we provide easily accessible high-quality annotation data to address the abovementioned obstacles. Data evaluation is performed by supervised learning using a deep convolutional neural network structure to segment 11 annotated PDAC histopathological whole slide images (WSIs) drawn by medical staff directly from an open WSI dataset. We visualized the segmentation results of the histopathological images with a Dice score of 73% on the WSIs, including PDAC areas, thus identifying areas important for PDAC diagnosis and demonstrating high data quality. Additionally, pathologists assisted by AI can significantly increase their work efficiency. The pathological AI guidelines we propose are effective in developing histopathological AI for PDAC and are significant in the clinical field.

Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification-Part 2: Pathology Whole Slide Image De-identification, De-facing, the Role of AI in Image De-identification, and the NCI MIDI Datasets and Pipeline.

De-identification of medical images intended for research is a core requirement for data sharing initiatives, particularly as the demand for data for artificial intelligence (AI) applications grows. The Center for Biomedical Informatics and Information Technology (CBIIT) of the United States National Cancer Institute (NCI) convened a two half-day virtual workshop with the intent of summarizing the state of the art in de-identification technology and processes and exploring interesting aspects of the subject. This paper summarizes the highlights of the second day of the workshop, the recordings and presentations of which are publicly available for review. The topics covered included pathology whole slide image de-identification, de-facing, the role of AI in image de-identification, and the NCI Medical Image De-Identification Initiative (MIDI) datasets and pipeline.

Highly tough slide-crosslinked gel polymer electrolyte for stable lithium metal batteries.

Gel polymer electrolytes (GPEs) hold great promise for the practical application of lithium metal batteries. However, conventional GPEs hardly resists lithium dendrites growth and maintains long-term cycling stability of the battery due to its poor mechanical performance. Inspired by the slide-ring structure of polyrotaxanes (PRs), herein we developed a dynamic slide-crosslinked gel polymer electrolyte (SCGPE) with extraordinary stretchability of 970.93% and mechanical strength of 1.15 MPa, which is helpful to buffer the volume change of electrodes and maintain mechanical integrity of the battery structure during cycling. Notably, the PRs structures can provide fast ion transport channels to obtain high ionic conductivity of 1.73×10-3 S cm-1 at 30°C. Additionally, the strong polar groups in SCGPE restrict the free movement of anions to achieve high lithium-ion transference number of 0.71, which is favorable to enhance Li+ transport dynamics and induce uniform Li+ deposition. Benefiting from these features, the constructed Li|SCGPE-3|LFP cells exhibit ultra-long and stable cycle life over 1000 cycles and high-capacity retention (89.6% after 1000 cycles). Even at a high rate of 16C, the cells deliver a high capacity of 79.2 mAh g-1. The slide-crosslinking strategy in this work provides a new perspective on the design of advanced GPEs for LMBs.

A novel Slide-seq based image processing software to identify gene expression at the single cell level.

Analysis of gene expression at the single-cell level could help predict the effectiveness of therapies in the field of chronic inflammatory diseases such as arthritis. Here, we demonstrate an adopted approach for processing images from the Slide-seq method. Using a puck, which consists of about 50,000 DNA barcode beads, an RNA sequence of a cell is to be read. The pucks are repeatedly brought into contact with liquids and then recorded with a conventional epifluorescence microscope. The image analysis initially consists of stitching the partial images of a sequence recording, registering images from different sequences, and finally reading out the bases. The new method enables the use of an inexpensive epifluorescence microscope instead of a confocal microscope.

High-Throughput Monitoring of Pathogenic Fungal Growth Using Whole Slide Imaging for Rapid Antifungal Susceptibility Assessment.

Invasive fungal infections are a major health threat with high morbidity and mortality, highlighting the urgent need for rapid diagnostic tools to detect antifungal resistance. Traditional culture-based antifungal susceptibility testing (AFST) methods often fall short due to their lengthy process. In our previous research, we developed a whole-slide imaging (WSI) technique for the high-throughput assessment of bacterial antibiotic resistance. Building on this foundation, this study expands the application of WSI by adapting it for rapid AFST through high-throughput monitoring of the growth of hundreds of individual fungi. Due to the distinct "budding" growth patterns of fungi, we developed a unique approach that utilizes specific cell number change to determine fungi replication, instead of cell area change used for bacteria in our previous study, to accurately determine the growth rates of individual fungal cells. This method not only accelerates the determination of antifungal resistance by directly observing individual fungal cell growth, but also yields accurate results. Employing Candida albicans as a representative model organism, reliable minimum inhibitory concentration (MIC) of fluconazole inhibiting 100% cells of Candida albicans (denoted as MIC100) was obtained within 3h using the developed method, while the modified broth dilution method required 72h for the similar reliable result. In addition, our approach was effectively utilized to test blood culture samples directly, eliminating the need to separate the fungi from whole blood samples spiked with Candida albicans. These features indicate the developed method holds great potential serving as a general tool in rapid antifungal susceptibility testing and MIC determination.

Digital whole-slide imaging of changes in amyloid after peripheral blood stem cell transplantation in patients with amyloid light-chain amyloidosis.

Peripheral blood stem cell transplantation (PBSCT) has made amyloid light-chain (AL) amyloidosis treatable. After PBSCT, hematological complete remission (HCR) can be achieved, leading to improved renal prognosis. The purpose of this study was to evaluate whether whole slide imaging of biopsy samples shows a post-treatment reduction in amyloid deposits in patients with AL amyloidosis. Patients were divided into three groups: Group A (n = 8), not eligible for PBSCT and treated with other therapies; Group B (n = 11), treated with PBSCT and achieved HCR; and Group C (n = 5), treated with PBSCT but did not achieve HCR. Clinical findings and amyloid deposition in glomeruli, interstitium, and blood vessels were compared before and after treatment using digital whole-slide imaging. Proteinuria and hypoalbuminemia improved more in Group B than in the other groups, and in Group B, amyloid deposition improved more in the glomeruli than in the interstitium and blood vessels. The long-term renal and survival prognosis was better in Group B than in the other groups. PBSCT can be expected to improve long-term clinical and renal histological prognosis in patients with AL amyloidosis who achieve HCR. Amyloid disappearance from renal tissue may take a long time even after clinical HCR.

Evaluating artificial intelligence-enhanced digital urine cytology for bladder cancer diagnosis.

BACKGROUND: This study evaluated the diagnostic effectiveness of the AIxURO platform, an artificial intelligence-based tool, to support urine cytology for bladder cancer management, which typically requires experienced cytopathologists and substantial diagnosis time. METHODS: One cytopathologist and two cytotechnologists reviewed 116 urine cytology slides and corresponding whole-slide images (WSIs) from urology patients. They used three diagnostic modalities: microscopy, WSI review, and AIxURO, per The Paris System for Reporting Urinary Cytology (TPS) criteria. Performance metrics, including TPS-guided and binary diagnosis, inter- and intraobserver agreement, and screening time, were compared across all methods and reviewers. RESULTS: AIxURO improved diagnostic accuracy by increasing sensitivity (from 25.0%-30.6% to 63.9%), positive predictive value (PPV; from 21.6%-24.3% to 31.1%), and negative predictive value (NPV; from 91.3%-91.6% to 95.3%) for atypical urothelial cell (AUC) cases. For suspicious for high-grade urothelial carcinoma (SHGUC) cases, it improved sensitivity (from 15.2%-27.3% to 33.3%), PPV (from 31.3%-47.4% to 61.1%), and NPV (from 91.6%-92.7% to 93.3%). Binary diagnoses exhibited an improvement in sensitivity (from 77.8%-82.2% to 90.0%) and NPV (from 91.7%-93.4% to 95.8%). Interobserver agreement across all methods showed moderate consistency (κ = 0.57-0.61), with the cytopathologist demonstrating higher intraobserver agreement than the two cytotechnologists across the methods (κ = 0.75-0.88). AIxURO significantly reduced screening time by 52.3%-83.2% from microscopy and 43.6%-86.7% from WSI review across all reviewers. Screening-positive (AUC+) cases required more time than negative cases across all methods and reviewers. CONCLUSIONS: AIxURO demonstrates the potential to improve both sensitivity and efficiency in bladder cancer diagnostics via urine cytology. Its integration into the cytopathological screening workflow could markedly decrease screening times, which would improve overall diagnostic processes.

Prediction of Tumor-Associated Macrophages and Immunotherapy Benefits Using Weakly Supervised Contrastive Learning in Breast Cancer Pathology Images.

The efficacy of immune checkpoint inhibitors is significantly influenced by the tumor immune microenvironment (TIME). RNA sequencing of tumor tissue can offer valuable insights into TIME, but its high cost and long turnaround time seriously restrict its utility in routine clinical examinations. Several recent studies have suggested that ultrahigh-resolution pathology images can infer cellular and molecular characteristics. However, few study pay attention to the quantitative estimation of various tumor infiltration immune cells from pathology images. In this paper, we integrated contrastive learning and weakly supervised learning to infer tumor-associated macrophages and potential immunotherapy benefit from whole slide images (WSIs) of H &E stained pathological sections. We split the high-resolution WSIs into tiles and then apply contrastive learning to extract features of each tile. After aggregating the features at the tile level, we employ weak supervisory signals to fine-tune the encoder for various downstream tasks. Comprehensive experiments on two independent breast cancer cohorts and spatial transcriptomics data demonstrate that the computational pathological features accurately predict the proportion of tumor-infiltrating immune cells, particularly the infiltration level of macrophages, as well as the immune subtypes and potential immunotherapy benefit. These findings demonstrate that our model effectively captures pathological features beyond human vision, establishing a mapping relationship between cellular compositions and histological morphology, thus expanding the clinical applications of digital pathology images.

Deep learning-based pathological prediction of lymph node metastasis for patient with renal cell carcinoma from primary whole slide images.

BACKGROUND: Metastasis renal cell carcinoma (RCC) patients have extremely high mortality rate. A predictive model for RCC micrometastasis based on pathomics could be beneficial for clinicians to make treatment decisions. METHODS: A total of 895 formalin-fixed and paraffin-embedded whole slide images (WSIs) derived from three cohorts, including Shanghai General Hospital (SGH), Clinical Proteomic Tumor Analysis Consortium (CPTAC) and Cancer Genome Atlas (TCGA) cohorts, and another 588 frozen section WSIs from TCGA dataset were involved in the study. The deep learning-based strategy for predicting lymphatic metastasis was developed based on WSIs through clustering-constrained-attention multiple-instance learning method and verified among the three cohorts. The performance of the model was further verified in frozen-pathological sections. In addition, the model was also tested the prognosis prediction of patients with RCC in multi-source patient cohorts. RESULTS: The AUC of the lymphatic metastasis prediction performance was 0.836, 0.865 and 0.812 in TCGA, SGH and CPTAC cohorts, respectively. The performance on frozen section WSIs was with the AUC of 0.801. Patients with high deep learning-based prediction of lymph node metastasis values showed worse prognosis. CONCLUSIONS: In this study, we developed and verified a deep learning-based strategy for predicting lymphatic metastasis from primary RCC WSIs, which could be applied in frozen-pathological sections and act as a prognostic factor for RCC to distinguished patients with worse survival outcomes.

Digital Cytology in Veterinary Education: A Comprehensive Survey of Its Application and Perception among Undergraduate and Postgraduate Students.

The COVID-19 pandemic accelerated technological changes in veterinary education, particularly in clinical pathology and anatomic pathology courses transitioning from traditional methods to digital pathology (DP). This study evaluates the personal effectiveness and satisfaction, as well as the advantages and disadvantages, of DP, in particular digital cytology (DC), as a teaching method among European veterinary students, both at the undergraduate and postgraduate level, who attended digital pathology courses during and before the pandemic. A further aim is to discuss the differences between the two student groups. A Google Form survey consisting of 11 multiple-choice questions was emailed to pathology teachers and distributed to their students. Results indicated that undergraduate students showed greater digital pathology training, favouring DC as the most effective learning modality. In contrast, postgraduate students reported less digital slide training, and their preference for learning cytology was split between DC alone and DC integrated with traditional microscopy. All students experienced whole slide imaging for learning cytology slides prevalently, and they stated that DC enhanced their learning experience. While DC demonstrates personal effectiveness and satisfaction as a teaching method, it is important to not replace pathology training with light microscopy completely, as almost a third of the students indicated.

Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images.

The presence of spread through air spaces (STASs) in early-stage lung adenocarcinoma is a significant prognostic factor associated with disease recurrence and poor outcomes. Although current STAS detection methods rely on pathological examinations, the advent of artificial intelligence (AI) offers opportunities for automated histopathological image analysis. This study developed a deep learning (DL) model for STAS prediction and investigated the correlation between the prediction results and patient outcomes. To develop the DL-based STAS prediction model, 1053 digital pathology whole-slide images (WSIs) from the competition dataset were enrolled in the training set, and 227 WSIs from the National Taiwan University Hospital were enrolled for external validation. A YOLOv5-based framework comprising preprocessing, candidate detection, false-positive reduction, and patient-based prediction was proposed for STAS prediction. The model achieved an area under the curve (AUC) of 0.83 in predicting STAS presence, with 72% accuracy, 81% sensitivity, and 63% specificity. Additionally, the DL model demonstrated a prognostic value in disease-free survival compared to that of pathological evaluation. These findings suggest that DL-based STAS prediction could serve as an adjunctive screening tool and facilitate clinical decision-making in patients with early-stage lung adenocarcinoma.

Prediction of Epidermal Growth Factor Receptor Mutation Subtypes in Non-Small Cell Lung Cancer From Hematoxylin and Eosin-Stained Slides Using Deep Learning.

Accurate assessment of epidermal growth factor receptor (EGFR) mutation status and subtype is critical for the treatment of non-small cell lung cancer patients. Conventional molecular testing methods for detecting EGFR mutations have limitations. In this study, an artificial intelligence-powered deep learning framework was developed for the weakly supervised prediction of EGFR mutations in non-small cell lung cancer from hematoxylin and eosin-stained histopathology whole-slide images. The study cohort was partitioned into training and validation subsets. Foreground regions containing tumor tissue were extracted from whole-slide images. A convolutional neural network employing a contrastive learning paradigm was implemented to extract patch-level morphologic features. These features were aggregated using a vision transformer-based model to predict EGFR mutation status and classify patient cases. The established prediction model was validated on unseen data sets. In internal validation with a cohort from the University of Science and Technology of China (n = 172), the model achieved patient-level areas under the receiver-operating characteristic curve (AUCs) of 0.927 and 0.907, sensitivities of 81.6% and 83.3%, and specificities of 93.0% and 92.3%, for surgical resection and biopsy specimens, respectively, in EGFR mutation subtype prediction. External validation with cohorts from the Second Affiliated Hospital of Anhui Medical University and the First Affiliated Hospital of Wannan Medical College (n = 193) yielded patient-level AUCs of 0.849 and 0.867, sensitivities of 79.2% and 80.7%, and specificities of 91.7% and 90.7% for surgical and biopsy specimens, respectively. Further validation with the Cancer Genome Atlas data set (n = 81) showed an AUC of 0.861, a sensitivity of 84.6%, and a specificity of 90.5%. Deep learning solutions demonstrate potential advantages for automated, noninvasive, fast, cost-effective, and accurate inference of EGFR alterations from histomorphology. Integration of such artificial intelligence frameworks into routine digital pathology workflows could augment existing molecular testing pipelines.

Towards in vitro - In vivo correlation models for in situ forming drug implants.

In vitro-In vivo correlation (IVIVC) is a main focus of the pharmaceutical industry, academia and the regulatory sectors, as this is an effective modelling tool to predict drug product in vivo performance based on in vitro release data and serve as a surrogate for bioequivalence studies, significantly reducing the need for clinical studies. Till now, IVIVCs have not been successfully developed for in situ forming implants due to the significantly different in vitro and in vivo drug release profiles that are typically achieved for these dosage forms. This is not unexpected considering the unique complexity of the drug release mechanisms of these products. Using risperidone in situ forming implants as a model, the current work focuses on: 1) identification of critical attributes of in vitro release testing methods that may contribute to differences in in vitro and in vivo drug release from in situ forming implants; and 2) optimization of the in vitro release method, with the aim of developing Level A IVIVCs for risperidone implants. Dissolution methods based on a novel Teflon shape controlling adapter along with a water non-dissolvable glass fiber membrane (GF/F) instead of a water dissolvable PVA film (named as GF/F-Teflon adapter and PVA-Teflon adapter, respectively), and an in-house fabricated Glass slide adapter were used to investigate the impact of: the surface-to-volume ratio, water uptake ratio, phase separation rate (measured by NMP release in 24 h post injection in vitro or in vivo), and mechanical pressure on the drug release patterns. The surface-to-volume ratio and water uptake were shown to be more critical in vitro release testing method attributes compared to the phase separation rate and mechanical pressure. The Glass slide adapter-based dissolution method, which allowed for the formation of depots with bio-mimicking surface-to-volume ratios and sufficient water uptake, has the ability to generate bio-relevant degradation profiles as well as in vitro release profiles for risperidone implants. For the first time, a Level A IVIVC (rabbit model) has been successfully developed for in situ forming implants. Release data for implant formulations with slightly different PLGA molecular weights (MWs) were used to develop the IVIVC. The predictability of the model passed external validation using the reference listed drug (RLD), Perseris®. IVIVC could not be developed when formulations with different PLGA molar ratios of lactic acid to glycolic acid (L/G) were included. The present work provides a comprehensive understanding of the impact of the testing method attributes on drug release from in situ forming implants, which is a valuable practice for level A IVIVC development.

Digital morphology network for effective teaching of cytology, histology and histopathology for medical and biology curriculum. VM3.0 Erasmus+ project.

INTRODUCTION: Digital microscopy transformation, the basis for the virtual microscopy applications, is a challenge but also a requirement in modern Medical Education. This paper presents the scope, background, methods, and results of the project "Digital Transformation of Histology and Histopathology by Virtual Microscopy (VM) for an Innovative Medical School Curriculum", VM3.0, funded by the European Union under the Erasmus+ framework (ref.no.2022-1-RO01-KA220-HED-000089017). The project was initiated at Grigore T. Popa University of Medicine and Pharmacy, Iași, Romania, with the support of Euroed Foundation, Iași, and cooperation of University partners from Gdansk (Poland), Plovdiv (Bulgaria), Alicante (Spain), and Patras (Greece) aimed to implement digital histology and histopathology teaching in a common network. MATERIALS AND METHODS: The backbone of the project was the development of a Digital Slide Platform based on the scans of histological slides collected from all the partners of the participating universities and the creation of a simple and fast digital/internet communication tool that could be used to improve histology and histopathology teaching of medical and natural sciences students. The construction of a Virtual Microscopy Library (VML) has been based on the acquisition of whole scans of high-quality histological slides stained by hematoxylin and eosin (H&E) and other classical staining methods and description of the details in English as well as respective languages of the project's partners. The VML can be used for different approches, both for students' instruction in classes as well as for individual students' work and self-testing. Universities from other countries could use the modal structure of the developed VML system on the condition that more slides are provided and the implementation of national language(s) is implemented. CONCLUSIONS: The combined efforts of all university partners allowed to establish the dynamic low-cost virtual microscopy educational system. The VM system could help unify the standards of cytology, histology, and histopathology teaching in a quest for the digital transformation of the European educational system.

Cell Culture and Slide Preparation for Cytogenetic Studies of Hematological Neoplasms.

Hematological neoplasms are heterogeneous diseases with various subtypes, each with its unique genomic features. Cell culture and slide preparation are essential steps to enrich and collect sufficient neoplastic cells for cytogenetic studies of the neoplasms. This chapter describes methods that are commonly used for culturing hematological neoplastic cells and preparing cytogenetic slides for clinical diagnosis and research of the neoplasms.

Fast and robust feature-based stitching algorithm for microscopic images.

The limited field of view of high-resolution microscopic images hinders the study of biological samples in a single shot. Stitching of microscope images (tiles) captured by the whole-slide imaging (WSI) technique solves this problem. However, stitching is challenging due to the repetitive textures of tissues, the non-informative background part of the slide, and the large number of tiles that impact performance and computational time. To address these challenges, we proposed the Fast and Robust Microscopic Image Stitching (FRMIS) algorithm, which relies on pairwise and global alignment. The speeded up robust features (SURF) were extracted and matched within a small part of the overlapping region to compute the transformation and align two neighboring tiles. In cases where the transformation could not be computed due to an insufficient number of matched features, features were extracted from the entire overlapping region. This enhances the efficiency of the algorithm since most of the computational load is related to pairwise registration and reduces misalignment that may occur by matching duplicated features in tiles with repetitive textures. Then, global alignment was achieved by constructing a weighted graph where the weight of each edge is determined by the normalized inverse of the number of matched features between two tiles. FRMIS has been evaluated on experimental and synthetic datasets from different modalities with different numbers of tiles and overlaps, demonstrating faster stitching time compared to existing algorithms such as the Microscopy Image Stitching Tool (MIST) toolbox. FRMIS outperforms MIST by 481% for bright-field, 259% for phase-contrast, and 282% for fluorescence modalities, while also being robust to uneven illumination.