Dr. Livia Sima, Romanian Academy, presents FLASH radiation therapy with short-pulse high-dose delivery and its effect on melanoma cells. The team used TissueFAXS iPLUS slide scanner for imaging the cells and TissueQuest for image analysis.
Dr. Andrea Papadopoulos and Trevor Khaba from Ndhlovu group, CESORA, Africa Health Research Institute, present their latest work on finding and characterizing new HIV reservoirs.
Dr. Lukas Kenner, Medical University of Vienna, presents his research using whole slide imaging on ambiguous role of STAT3 signaling pathway in prostate cancer progression and therapeutic potential.
Dr. Amirreza Mahbod, Danube Private University, discusses the development and implementation of a deep learning algorithm for improved nuclei segmentation in microscopic images.
Dr. Rupert Ecker, CEO of TissueGnostics, discusses the possibilities of precision microscopy and tissue cytometry in oncology research in the webinar at the TRI (Translational Research Institute) Cancer Seminar.
Prof. Nicolas C. Hoch, University of Sao Paulo, is presenting his research on signaling and repair of DNA damage. He and his group are utilizing the power of TissueFAXS i PLUS slide scanning and the high-end image analysis solution, StrataQuest, for their research.
Dr. Rupert Ecker, CEO of TissueGnostics, gives a short introduction of TissueGnostics’ tissue cytometry solutions with the main focus on immunophenotyping and spatial analysis of the tumor microenvironment in-situ.
Collaborative webinar series with PreciPoint GmbH: From Tissue Classification to Proximity Measurement - Why to Use AI Software in Histopathology Research? 3-part Series. Learn more about TG powered comprehensive tissue cytometry.
Collaborative webinar series with PreciPoint GmbH: From Tissue Classification to Proximity Measurement - Why to Use AI Software in Histopathology Research? 3-part Series. Learn more about TG powered machine learning-based tissue classification.
Dr. Gregory CG Hugenholtz, Department of Surgery, University Medical Center Groningen, presents "TAFI deficiency promotes liver damage in murine models of liver failure through defective down-regulation of hepatic inflammation".
Prof. Florian Gruber and Christopher Kremslehner, MSc from Medical University of Vienna, Austria introduce their newly developed Metabolic Imaging strategy published in the journal Redox Biology.
CANCER RESEARCH UK (CRUK) - CAMBRIDGE CENTRE invited our CEO Rupert Ecker, TissueGnostics to give a talk about "Contextual Tissue Cytometry with AI - Immunophenotyping and Quantifying the Tumour Microenvironment".
At the Image Analysis Training School 2020, Nottingham Molecular Pathology Node, Dr Rupert Ecker presents the power of TissueGnostics' in situ Immunophenotyping and AI-based Histomorphometry analysis solutions.
In this webinar Dr. Kim RM Blenman, Yale School of Medicine, USA, presents her integration of TissueGnostics’ tissue cytometers and the contextual image cytometry software StrataQuest in a unique in-silico multiplexing workflow.
Dr. Qu Jiang, University Hospital of Leipzig, presents her research based on a recent paper on the role of the demethylase KDM1A in chronic lymphocytic leukemia (CLL) and discusses the effects of its knockdown.
Dr. Lukas Kenner, Medical University of Vienna, discusses the impact of micro- and nanoplastic particles on human health, including their potential effect on cancer development.
Dr. Henning Ulrich , University of Sao Paulo, talks about applications of tissue cytometry in neuroscience. He presents his research on amyloid plaques and neuroinflammation in an Alzheimer's disease.
Dr. Nidhi S. Dey , presents her exciting work on the “Spatial analysis of IDO1 and PDL1 checkpoints in cutaneous leishmaniasis”. The study shown focuses on uncovering potential targets, also known as immune checkpoints, for the treatment strategy for leishmaniasis.
Nadine Bayer, PhD, presents her paper “Disturbances in microbial skin recolonization and cutaneous immune response following allogeneic stem cell transfer”, which was recently published in the high-impact journal Stem Cell Transplantation.
Dr. Thomas Diefenbach, The Ragon Institute Boston, presents various approaches for multiplexing IF, utilizing the power of TissueFAXS high-throughput automated slide scanning and StrataQuest powered high-end image analysis.
The topic covered by the webinar was recently published by Vinel et al. in Nature Communications and it addresses a comparative epigenetic analysis of tumour initiating cells and syngeneic EPSC-derived neural stem cells in glioblastoma.
Collaborative webinar series with PreciPoint GmbH: From Tissue Classification to Proximity Measurement - Why to Use AI Software in Histopathology Research? 3-part Series. Learn more about TG powered deep learning-based nuclei segmentation.
Dr. Axel Dievernich (RWTH Aachen) presents the results of his recently published paper. It addresses the characterization of the adaptive and innate immune cells involved in foreign human body reaction.
In this webinar Dr. Kim RM Blenman, Yale School of Medicine, USA, is focusing on the characterization of spatial dynamics of TIME components through machine learning and deep neural networks for high content phenotyping (EACR Congress, June 2021).
Dr. Johanna Strobl, PhD, from the Medical University of Vienna, Austria talks about her recently published study addressing host-derived skin-resident memory T cells as potential players in human graft versus host disease.
TG and De Novo Software started to collaborate by combining expertise in Image Cytometry. This webinar will teach researchers about the new streamlined workflow that enables scientists to quickly obtain publication-ready results from IF/IHC tissue sections.
Dr. Rupert Ecker, CEO of TissueGnostics, presents the TissueFAXS cytometry platform. TissueGnostics is a solution provider for Precision Medicine / Next-Generation Digital Pathology and produces fully integrated cutting-edge tissue cytometers.
Cytometry encompasses several methods for investigating cells, such as their count, cell cycle state, phenotype, morphology, size ...
We have a lot of exciting plans to share for our big birthday, but we also have many important milestones to reflect on and remember...
The IHC Immune Status in Situ App uses the AI classifier to segment tissue into morphological entities such as tumor, stroma, and lymphocyte clusters. It further identifies single cells ...
Explore our online database of reference publications to find tout how tissue cytometry can elevate your research
Explore our vast catalog of analysis solutions in our Strataquest Appcenter and get inspired by the variety of applications.
Read about tissue cytometry's potential in understanding the complexities of cell populations in the tissue environment
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The IF Skeletal Muscle App allows the segmentation of skeletal muscle tissue sections into muscle fibers and connective tissue based on specific IF staining. Outcome parameters are provided, such as the number of muscle fibers and area of the total tissue, muscle fibers, and connective tissue.
Image: Courtesy of Stefania Petrini, Bambino Gesù Children’s Hospital, Rome
App Category 2
The IHC Adipocyte App quantifies adipocytes and their lumen in adequate HE samples. The App automatically mends small rips in adipocyte membranes and eliminates cell membrane artifacts in adipocyte lumina and lumina on sample borders. The App also outputs area measurements for all detected adipocyte lumina.
App Category 1
The IF 2 App provides single cell-based co-expression analysis for two IF markers. It segments cells into their nucleus, perinuclear area, and/or cytoplasm. Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters that can be displayed in and exported into scattergrams and histograms.
App Category 1
The IF 3 App provides single cell-based co-expression analysis for three IF markers. It segments cells into their nucleus, perinuclear area, and/or cytoplasm. Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters that can be displayed in and exported into scattergrams and histograms.
App Category 1
The IF 4 App provides single cell-based co-expression analysis for four IF markers. It segments cells into their nucleus, perinuclear area, and/or cytoplasm. Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters that can be displayed in and exported into scattergrams and histograms.
App Category 2
The IF Glomeruli App provides the detection of tissue, cells, and glomeruli stained by a specific marker. It segments the cells into their nucleus and/or cytoplasm and determines the cellular phenotype of specific IF-stained cell populations. The detected cells can be classified as being either inside or outside the glomeruli within certain distances (distance ranges are definable). For each cell, the spatial information and up to 20 intensity, statistics, and morphometric parameters are measured. The data can be displayed in diagrams and exported.
App Category 3
The IF Hi-Plex 50 App combines and analyses images of the same IF-stained tissue section, acquired up to 50 times with different markers. The App enables the detection of the cellular phenotypes of specific IF-stained cell populations. It segments cells into their nucleus, perinuclear area, and/or cytoplasm. Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters that can be displayed in and exported into scattergrams and histograms.
App Category 3
The IF Dots App provides dot detection per cell within the cell compartments for up to four markers in a sample (e.g., FISH, RNA, oil droplets). Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters. Dot measurement parameters are provided per cell compartment (e.g., nucleus, cytoplasm) and per dot and include count, mean intensity, total dot area, the sum of intensity.
App Category 2
The IF Immune Status in Situ App provides a phenotypic characterization of immune cells in reference to detected metastructures (e.g., tumors, glands) and measures the distance of detected cellular objects to the metastructure boundary (within and/or outside). Distance ranges are also definable. Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters, as well as the distance of each cell to the areas boundary.
App Category 3
The IF Skin Morphology App provides tissue detection, including the segmentation of epidermis and dermis, based on specific IF staining. It segments cells into their nucleus, perinuclear area, and/or cytoplasm and determines the cellular phenotype of specific IF-stained cell populations. The detected cells can be classified and visualized as being within or outside detected structures (epidermis and dermis). Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters that can then be displayed in diagrams and exported.
App Category 3
The IF Granuloma App detects granulomas based on nuclear structure analysis and an adequate IF staining (e.g., CD11c, CD68). The App measures the number and area of Granulomas and their density. Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters.
App Category 2
The IF Cytoskeleton App detects cytoskeletal structures based on a specific stain. The cell cytoplasm can be detected using other stains. Data can also be exported, including the number of cytoskeletal filaments inside and outside the cell and on the cell membrane, filament length, and total filament area.
App Category 3
The IF Glial Cells App allows the detection of astrocytes and microglia based on specific IF staining. The measurements assessed by the App include the number of astrocytes and microglia and the number of branches of each cell (short and long attached).
App Category 3
The IHC 2 App unmixes two markers (e.g., chromogen and counterstain) in an IHC or HC digital slide and segments single cells into their nucleus, perinuclear area, and/or cytoplasm. Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters that can be displayed in and exported into scattergrams and histograms.
App Category 1
The IHC 3 App unmixes three markers (e.g., two chromogens and a counterstain) in an IHC or HC digital slide and segments single cells into their nucleus, perinuclear area, and/or cytoplasm. Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters that can be displayed in and exported into scattergrams and histograms.
App Category 2
The IHC Macrophages App detects macrophages based on adequately stained IHC samples. The App can be combined with area detection and distance range algorithms to determine the distance of Langerhans cells from the border of the epidermis inside and outside the epidermis (see above example). Each segmented cell compartment is measured for up to 20 parameters, as is the distance of each cell to the boundary.
App Category 3
The IHC Tumor-Stroma App combines the segmentation of tumor and stroma (based on the morphology) and the detection of specifically stained cell populations. It segments the cells into their nucleus, perinuclear area, and/or cytoplasm. Each segmented cell compartment in tumor and/or stroma is measured for up to 20 intensity, statistic, and morphometric parameters that can be displayed in and exported into scattergrams and histograms.
App Category 2
The Pulmo App segments nuclei and the metastructure components of the lung, including tissue, bronchioles, blood vessels, and alveoles. Each segmented metastructure is measured for up to 20 morphometric parameters.
App Category 3
The IHC Angio App detects blood vessels based on appropriate stains (e.g., CD31) and measures overall vessel and lumen areas. The vessel detection can be set to close open stained vessel walls and connect separated vessel sections within a definable distance. As well as vessel number, density, and areas, the App also outputs endothelium and lumina areas.
App Category 2
The IHC Meta Cells App combines the detection of IHC/HC stained metastructures (e.g., Langerhans islets, Tumor - Stroma) with single-cell detection (segmentation of cells into nucleus, perinuclear area, and/or cytoplasm). Detected cells can be classified and visualized as being within or outside detected metastructures. Each detected area and cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters.
App Category 2
The RNA Scope App enables the detection of nuclei based on appropriate staining and dot detection per cell within nucleus and/or cytoplasm for one dot marker in CISH and SISH experiments . Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters. Dot parameters are provided per cell and per dot and include count, mean intensity, total dot area and the sum of intensity.
App Category 2
The Tumor Foci App allows for the detection of the whole tissue and, more importantly, tumor foci based on nuclear structure analysis, mainly on HE staining. The number, area, and density of tumor foci are measured.
App Category 1
The IHC Membrane App unmixes up to three markers in an IHC or HC digital slide and segments cells into nucleus, perinuclear area, and/or cytoplasm, as well as into membrane (e.g., HER2/neu). Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters. Three more parameters are measured for membrane intensity and angle of staining. All parameters are displayed in scattergrams and histograms and can be exported.
App Category 2
The Bone Tissue Analysis Goldner App allows for the detection of mineralized bone tissue and osteoid based on Goldner-stained bone tissue sections. The App assesses parameters such as BV (bone volume), TV (trabecular bone volume), OV (osteoid volume), OV/BV, OV/TV, OS (osteoid surface), BS (bone surface length), and the mean of osteoid width and thickness.
App Category 2
The Bone Mineralization APP separates Safranin O-stained bone tissue into its morphological substructures (cartilage, mineralized cartilage, bone marrow, and mineralized bone). Measurements assessed with this App include TV (trabecular bone volume), BV (total bone volume), MCV (mineralized cartilage), CV (cartilage volume), and bone marrow (BM).
App Category 3
The Bone Tissue Analysis Von Kossa App allows for the detection of mineralized bone tissue based on Von Kossa stained bone tissue sections. The App provides parameters such as TV (trabecular bone volume), BV (bone volume), BS (bone surface), BV/TV, BS/BV, Tb.N (trabecular number), tb.Th (trabecular thickness), and Tb.Sp (trabecular separation).
App Category 2
The IF Leishmaniasis App detects intracellular Leishmania parasites and segments them in the detected host cells. The number of parasites per cell is determined, and living and dead parasites can be distinguished (live/dead assays). The App outputs 20 intensity, statistic, and morphometric parameters for each segmented cell compartment per marker, as well as the number, mean intensity, sum of intensity, and size of parasites.
App Category 2
The IF Cellular Microenvironment App allows to determine the cellular phenotype of specific IF-stained cell populations and establishes their spatial relationship between each other and their neighboring cells/cell populations, including those with metastructures (e.g., blood vessels, tumors) in their vicinity. It is especially suited for proximity and infiltration analyses.
App Category 4
The IF Neurite App identifies neuronal cells and cell clusters and their neurites/dendrites. It quantifies the number of neurites/dendrites branching out from a specific neuron, identifies branch points, and exports total neurite/dendrite area, total neurite/dendrite length, average neurite/dendrite thickness, the number of branch points, and the number of endpoints.
App Category 3
The IF Pyknotic Nuclei App provides tissue detection and cell segmentation in combination with the detection of pyknotic nuclei (defined as completely condensed, round, high-intensity nuclei) based on nuclei staining. Additionally, the App allows for the determination of the cellular phenotype of specific IF-stained cell populations and dot detection. It segments the detected cells into nucleus, perinuclear area, and/or cytoplasm. The App provides parameters such as the number, mean intensity, and percentage of specific cell populations (including cells containing pyknotic nuclei). It also outputs dot parameters per segmented cell and/or dot, including count, mean intensity, total dot area, and sum of intensity.
App Category 3
The IF Tumor Vascularization App provides tissue detection, including the separation of tumor tissue and tumor stroma (healthy tissue). Additionally, it detects blood vessels based on appropriate stains (e.g., CD31) and measures the number, area, and density of these blood vessels. The vessel detection also can be set to close open stained vessel walls and to connect separated vessel sections within a definable distance.
App Category 3
The IHC Tumor-Macrophages App provides tissue detection, including the separation of tumor and healthy tissue. It detects macrophages based on specific staining (e.g., CD68) and outputs the area of macrophages within tumor and healthy tissue.
Image: Courtesy of Dr. Patrick Michl, Dr. Maren Egidi, and Dr. Heidi Griesmann, Universitätsklinikum Halle (Saale).
App Category 3
The IHC Tumor Vascularization App provides tissue detection, including the separation of tumor tissue and tumor stroma (healthy tissue). Additionally, it detects blood vessels based on appropriate stains (e.g., CD31) and measures the number and area of these blood vessels. The vessel detection can also be programmed to close open stained vessel walls and to connect separated vessel sections within a definable distance. The App outputs the number, density, and areas of vessels within both tumor and healthy tissue.
Image: Courtesy of Dr. Patrick Michl, Dr. Maren Egidi, and Dr. Heidi Griesmann, Universitätsklinikum Halle (Saale).
App Category 3
The IHC Angio Trichome App detects blood vessels based on trichome staining and measures the overall vessel and lumen area. Furthermore, it detects specifically IHC-stained single cell populations and establishes their spatial relationship to the detected blood vessels. The App outputs number and vessel density, vessel wall thickness and areas of vessels, endothelium and lumina, the number of IHC stained cells, proximity measurement, etc.
App Category 3
The RNA Scope+ App provides detection of nuclei based on appropriate staining and dot detection per cell within the nucleus and/or cytoplasm for two dot markers in CISH and SISH experiments. Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters. It also outputs dot parameters per segmented cell and/or dot, including count, mean intensity, total dot area, and sum of intensity.
App Category 2
The IHC Angio Elastin App detects blood vessels in Verhoeffs van Geison-stained samples, elastin, and collagen. The outputs include the number and area of vessels, elastin, and collagen within a definable distance to the vessel.
App Category 2
The IF Cardio Cell Culture App provides cell segmentation, detection of cardiomyocytes (based on appropriate staining, e.g., Troponin Red), fibroblasts within cultured cardio cells, plus one additional marker. The App outputs parameters such as the number of cardiomyocytes, fibroblasts, and marker-positive cardiomyocytes and fibroblasts.
Image: Courtesy of Agatha Ribeiro da Silva, Prof. Jose E. Krieger (Heart Institute, University Sao Paulo)
App Category 3
The IF Cardio Cell Culture Dot App provides cell segmentation and detection of cardiomyocytes (based on an appropriate stain, e.g., Troponin Red) and fibroblasts within cultured cardio cells, plus one dot marker (CISH, FISH). The App outputs parameters such as the number of cardiomyocytes and fibroblasts. It also outputs the number of dot-positive cardiomyocytes and fibroblasts and the number, area (μm²), and mean intensity of dots per cell.
Image: Courtesy of Agatha Ribeiro da Silva, Prof. Jose E. Krieger (Heart Institute, University Sao Paulo).
App Category 3
The IF Dendrites & Axons App identifies neuronal cells, their dendrites and axon, based on appropriate markers. It quantifies the number of dendrites branching out from a specific neuron. The App provides the total number of dendrites per neuron, including the length of these dendrites and their axons.
Image: Courtesy of Thomas Bastian, Ph.D., University of Minnesota.
App Category 3
The IHC Small Intestine - Dots App provides nuclei segmentation and detection of tissue and villi based on nuclei staining (crypts need to be defined manually). Furthermore, it allows dot detection for one dot markers (CISH, RNAScope, SISH) within villi and crypt areas. Dot parameters are provided for villi and crypts and for dots and include count, mean intensity, total dot area, the sum of intensity.
App Category 2
The Lipid Droplets App quantifies lipid droplets within H&E stained tissues (e.g., liver). The App automatically mends small rips in liver droplet membranes and eliminates cell membrane artifacts, including lumina on sample borders. The App also outputs area and number measurements for all detected lipid droplets.
App Category 1
The Angio Sirius Red App detects collagen and blood vessels based on Sirius Red staining. The app outputs the area of Sirius Red stained collagen and the number of detected vessels.
App Category 2
The IHC Angio Diameter App detects blood vessels based on appropriate stains (e.g., CD31). The App outputs vessel area, number, density, and blood vessel diameter.
App Category 2
The IF Cultured Cells & Substructures App detects cells based on nuclei staining, as well as one dot marker (FISH, CISH experiments) and cytoskeletal structures based on a specific stain. It outputs the number of detected cells, the number and intensity of dots per cell, and the density of cytoskeletal filaments.
App Category 3
The IF Membrane App detects nuclei and segments the cells into different cellular compartments, including membrane, nuclei, and cytoplasm. It also detects one additional marker (e.g., HER2/neu). Each segmented cell compartment is measured for different parameters, such as staining intensity, stained area, and the number/percentage of marker-positive cells within the detected cellular compartments. Three more parameters are measured for the membrane, including membrane area, membrane length, and the angle of staining.
App Category 2
The IF Spheroids App allows a comprehensive analysis of spheroids (as well as organoids and embryoid bodies). It automatically identifies the spheroids and cells based on the nuclei staining and analyzes two additional IF markers. It segments the cells into different cellular compartments, including membrane, nuclei, and cytosol, and further measures the marker expression for each compartment. It can also measure dot markers (if available). It establishes proximity distances for the cells detected within the spheroids, bringing the IF-stained cell populations into spatial context.
App Category 3
The IHC Immune Status in Situ App uses the AI classifier to segment tissue into morphological entities such as tumor, stroma, and lymphocyte clusters. It further identifies single cells based on nuclei staining (hematoxylin), detects immune cells based on appropriated stains (CD45, CD3, CD20, etc.), and measures the distance of detected cells to the metastructure boundary. The App can also define distance ranges through outputting parameters, including the area of the detected morphological entities and the number/percentage of lymphocytes detected within the tissue entities, as well as in certain proximities.
App Category 3
The Wilms Tumor App is based on the AI Classifier and allows for the segmentation of H&E stained Wilms tumor tissues into tumor, stroma, and blood vessels. It outputs the area (µm2) of the segmented tissue entities.
App Category 1
The IHC Necrotic Tumor App segments tumor tissues into tumor, necrotic tissue, and stroma using the AI Classifier. Furthermore, it identifies single cells as well as one additional cellular marker (e.g., neutrophils). It outputs the area of tumor, necrotic tissue, and stroma and measures the number and percentage of neutrophils within the morphological entities.
App Category 3
The IHC Necrotic Tumor Angio App can segment tumor tissues into tumor, necrotic tissue, and blood vessels using the AI Classifier. It outputs the area of tumor, necrotic tissue, and blood vessels, as well as the number and percentage of blood vessels in total and within the two morphological entities.
App Category 3
The IF Rods & Cones in Retina App detects the rods and cones based on specific staining. It outputs the number, density, and length of detected structures, as well as the number, percentage, and density of marker-stained rods and cones.
App Category 2
The IF Tumor Foci Angio App identifies single cells and segments tissues into tumor foci and blood vessels based on appropriate markers. It applies proximity maps to identify nuclei close to blood vessels. It measures the number of nuclei located within a certain distance relative to blood vessels, the number of nuclei in the different morphological entities, and the area of these morphological entities.
App Category 3
The IF Retinal Vasculature App detects blood vessels in retinal tissue based on appropriate stains (e.g., CD31). The App outputs vessel area, density, and length.
App Category 2
The Endometrium HE App allows for the segmentation of hematoxylin and eosin-stained endometrium tissues into their morphological entities (glands, stroma, and blood vessels). The measurements provided by the App include the area of glands, stroma, and blood vessels.
App Category 2
The IHC Extracellular Filament App detects nuclei and extracellular filaments stained with specific markers. It outputs the number of nuclei, total filaments area, and the length of these filaments.
App Category 2
The IF Cellular Contact App allows for the determination of the cellular phenotype of specific IF-stained cell populations and establishes the cellular contacts to their neighboring cells (the number of markers is technically unlimited). If needed, the App provides a separation of nuclei in tissues with high cellular densities. The App outputs parameters such as staining intensity per marker and morphometric parameters for each segmented cell/cell compartment, as well as the number and percentage of cells of different phenotypes in direct contact.
Images: courtesy of Naoki Kaneko/Shiv Pillai (PI), Ragon Institute of MGH, MIT and Harvard, Boston, MA USA.
App Category 3
The App was used in a CELL publication, read more
The EBER-ISH App analyzes tissue samples stained by EBER-ISH probes (EBV-encoded RNA in-situ hybridization). These probes visualize the Epstein-Barr virus (EBV) EBER RNA. First, the App identifies nuclei, then detects EBER-ISH positive nuclei. The measurements provided by the App include the number of detected cells and the number, density, and percentage of EBER-ISH positive cells.
App Category 1
The IF Cell Culture - Osteoclast App allows for the segmentation of nuclei, the identification of cultured multinucleated osteoclasts (stained by a specific marker), and the quantification of one or two additional markers. Outputs include the number of detected cells, osteoclasts, and nuclei per osteoclast, the area of osteoclasts, and the intensity of markers within the osteoclasts.
App Category 2
read more about Automated Detection and Characterization of Osteoclasts in Microscopic Images
The Organoid App detects cultured organoids using the machine learning classifier. It outputs number and total area (µm2) of organoids and categorizes them into different size classes.
App Category 3
The IF Brain App allows the classification (using the AI based classifier) of brain regions and detection of various cellular phenotypes, e.g. astrocytes, based on stained linage markers. The App outputs area (µm2) of detected tissues classes, count of total cells as well as in each detected area. Count and % of specific phenotype detected in total as well as in tissue classes.
App Category 3
The Embryoid Bodies App automatically detects embryoid bodies/organoids based on IF staining. It identifies nuclei based on DAPI staining or other nuclei dye and identifies additional phenotype markers in the nuclei/cell and/or membrane of the detected cells. It outputs number and area (µm2) of detected embryoid bodies/organoids, count of nuclei and count/% of cellular phenotypes.
App Category 2
The IF Insulin Islet App allows for detection marker stained insulin islets, the whole tissue, and cellular phenotypes stained by specific markers within the insulin islets. The App outputs, whole tissue area (µm2), number and area (µm2) of detected insulin islets. Number of cells and marker-specific phenotypes in the whole tissue as well as within the insulin islets. Image provided by Emma Hamilton-Williams.
App Category 3
The Mucin Swiss Roll App allows for detection of the swiss roll, and the segmentation into different subclasses (mucosa, immune cell follicles, connective tissue, background). Further it detects nuclei and (e.g. PAS stained) mucin. The App outputs area (µm2) of detected tissues/tissue classes, count of total cells and in each detected area as well as the area of stained mucin in the entire tissue and within the subclasses. Image Courtesy: Priv.-Doz.Dr. Martin Schepelmann
App Category 2
The IHC Swiss Roll App allows for detection of the swiss roll, and the segmentation into different subclasses (mucosa, immune cell follicles, connective tissue, background). Further it detects nuclei and identifies phenotypes based on specific stains. The App outputs area (µm2) of detected tissues/tissue classes, count of total cells as well as in each detected area. Count and % of specific phenotype detected in total as well as in the tissue classes. Image Courtesy: Priv.-Doz.Dr. Martin Schepelmann
App Category 2
The IF Swiss Roll App allows for detection of the swiss roll, and the segmentation into different subclasses (mucosa, immune cell follicles, connective tissue, background). Further it detects nuclei and identifies phenotypes based on specific IF stains. The App outputs area (µm2) of detected tissues/tissue classes, count of total cells as well as in each detected area. Count and % of specific phenotype detected in total as well as in the tissue classes. Image Courtesy: Priv.-Doz.Dr. Martin Schepelmann
App Category 2
The IHC Insulin Islet App allows for detection of marker stained insulin islets, the whole tissue, and cellular phenotypes stained by specific markers within the insulin islets and in the tissue. The App outputs, whole tissue area (µm2), number and area (µm2) of detected insulin islets. Number of cells and marker-specific phenotypes in the whole tissue as well as within the insulin islets.
App Category 2
The IHC Adipocytes+ App identifies adipocytes and cellular aggregates inbetween the adipocytes. Small rips in adipocyte membranes are mended automatically and cell membrane artefacts in adipocyte lumina are automatically eliminated. The App outputs number and area measurements for all detected adipocytes as well as number and area of cellular aggregates.
App Category 2
The IHC Microglia App detects microglia soma based on a specifc staining and further identifies branches as well as primary and secondary branching points. It outputs number and area of cells, number of primary and secondary branching points as well as area and lenght of the detected branches.
App Category 3
The IHC Lung Cancer mouse App is using the machine learning base classifier to segment murine lung cancer tissue sections into tumor and non-cancerous tissue. Further it detects nuclei based on hematoxylin staining and identifies cellular phenotypes based on speicifc markers. It outputs number and area of tumor tissue as well as total number of cells and detected cellular phenotypes.
App Category 3
The IHC Megakaryocytes App allows for detection of megakaryocytes based on specific marker staining. It outputs number and size of detected megakaryocytes as well as the number of megakaryocytes that contain neutrophils inside their cytoplasm and the number of neutrophils inside each megakaryocyte. Image courtesy: Prof Wendy Erber and A/Prof Kathy Fuller, The University of Western Australia
App Category 2
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