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92 results for “Histological image”
Figs. 47-53 Magnolia stellata. Figs. 47–49 SEM images. Figs. 50–53 Histological sections. Figs. 47–49 in Nectar production in the pollen flower of Anemone nemorosa in comparison with other Ranunculaceae and Magnolia (Magnoliaceae)
Figs. 47-53 Magnolia stellata. Figs. 47–49 SEM images. Figs. 50–53 Histological sections. Figs. 47–49 Surface of the carpels is covered with solidified nectar. Figs. 48–49 Stylar surface at higher magnification; arrow stoma for gas exchange. Fig. 50 Longitudinal section
Dataset for "Segmentation of Lipid Droplets in Histological Images"
<p>Datasets for the publication "Segmentation of Lipid Droplets in Histological Images" (Medical Imaging with Deep Learning (MIDL 2023), short paper track. 2023. https://openreview.net/forum?id=nTnAm_El0RC)</p>
Bone Erosions in Rheumatoid Arthritis - Characterization Evaluated by Imaging and Histology
ClinicalTrials.gov study NCT04645381. IPD Sharing: NO. Countries: 1. Publications: 1.
BLI (Blue Light Imaging) for the Histological Characterization of Colorectal Polyps
ClinicalTrials.gov study NCT03274115. IPD Sharing: NO. Countries: 1. Publications: 1.
Acute Myocarditis Registry With Prognostic, Histologic, Immunologic, Biological, Imaging and Clinical Assessment
ClinicalTrials.gov study NCT04844151. IPD Sharing: UNDECIDED. Countries: 1. Publications: 23.
A Phase 1 Pilot Study of 99mTc-MIP-1404 SPECT/CT Imaging to Histology in Men With Prostate Cancer
ClinicalTrials.gov study NCT01615406. IPD Sharing: Not stated. Countries: 1. Publications: 16.
Histological Validation Of Carotid Plaque Composition In Preoperative Imaging
ClinicalTrials.gov study NCT01456403. IPD Sharing: NO. Countries: 1. Publications: 1.
Artificial Intelligence-based Techniques to Characterize KIdney Microstructure on Histological ImagEs
ClinicalTrials.gov study NCT06690190. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Malignant Myoepithelioma of Bone and Soft Tissues: Diagnostic Imaging and Histology in Relation to Prognosis
ClinicalTrials.gov study NCT06244420. IPD Sharing: NO. Countries: 1. Publications: 2.
ParisK: Correlation of Imaging Techniques With Histology
ClinicalTrials.gov study NCT01709045. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Learning MRI and Histology Image Mappings for Cancer Diagnosis and Prognosis.
ClinicalTrials.gov study NCT04792138. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Preoperative Identification of the Histologically "Vulnerable" Plaque Using Non-invasive Imaging, Biomechanical Assessment and Baroreflex Evaluation in Patients With Severe Carotid Stenosis
ClinicalTrials.gov study NCT05566080. IPD Sharing: UNDECIDED. Countries: 1. Publications: 13.
Deep learning models to detect microsatellite instability in colorectal cancer from histological images
<p>This repository contains trained deep learning models (based on shufflenet) for detecting microsatellite instability (MSI) in histological images of colorectal cancer. Expected input is 512x512x3, output is categorical (MSI vs. not MSI).</p> <p>The models are provided as MAT (Matlab R2019a) files. All models are described in detail in the publication "Clinical-grade detection of microsatellite instability in colorectal cancer by deep learning: an international multicenter study"</p> <p>More information on the original model: <a href="https://www.mathworks.com/help/deeplearning/ref/shufflenet.html">https://www.mathworks.com/help/deeplearning/ref/shufflenet.html</a></p>
Synthetic histology images of colorectal cancer, generated by conditional generative adversarial networks
<p>These are generated (synthetic) histology images of colorectal cancer. These images were generated by conditional GANs and are in two classes: MSIH (microsatellite instable high) and nonMSIH. There are two sets: one set with 10K images per class and another one with 75K images per class. All images are RGB, 512x512 px at a resolution of 0.5 micrometers per pixel. For more information, please stay tuned for our upcoming manuscript on www.kather.ai. </p>
Data from: Robust extraction of quantitative structural information from high-variance histological images of livers from necropsied Soay sheep
Quantitative information is essential to the empirical analysis of biological systems. In many such systems, spatial relations between anatomical structures is of interest, making imaging a valuable data acquisition tool. However, image data can be difficult to analyse quantitatively. Many image processing algorithms are highly sensitive to variations in the image, limiting their current application to fields where sample and image quality may be very high. Here, we develop robust image processing algorithms for extracting structural information from a dataset of high-variance histological images of inflamed liver tissue obtained during necropsies of wild Soay sheep. We demonstrate that features of the data can be measured in a fully automated manner, providing quantitative information which can be readily used in statistical analysis. We show that these methods provide measures that correlate well with a manual, expert operator-led analysis of the same images, that they provide advantages in terms of sampling a wider range of information and that information can be extracted far more quickly than in manual analysis.
Figure 8 from: Jochum A, Slapnik R, Klussmann-Kolb A, Páll-Gergely B, Kampschulte M, Martels G, Vrabec M, Nesselhauf C, Weigand AM (2015) Groping through the black box of variability: An integrative taxonomic and nomenclatural re-evaluation of Zospeum isselianum Pollonera, 1887 and allied species using new imaging technology (Nano-CT, SEM), conchological, histological and molecular data (Ellobioidea, Carychiidae). Subterranean Biology 16: 123-165. https://doi.org/10.3897/subtbiol.16.5758
Figure 8 - NanoCT−SEM Plate 2. A–F Zospeum isselianum, neotype (CSR SASA 37013), Turjeva jama G–H Zospeum isselianum (SMNH 2216), Jama na Zgornjih Brsnikih I–L Zospeum sp. (CSR SASA 37698a), Konečka zijalka.
Figure 2 from: Jochum A, Slapnik R, Klussmann-Kolb A, Páll-Gergely B, Kampschulte M, Martels G, Vrabec M, Nesselhauf C, Weigand AM (2015) Groping through the black box of variability: An integrative taxonomic and nomenclatural re-evaluation of Zospeum isselianum Pollonera, 1887 and allied species using new imaging technology (Nano-CT, SEM), conchological, histological and molecular data (Ellobioidea, Carychiidae). Subterranean Biology 16: 123-165. https://doi.org/10.3897/subtbiol.16.5758
Figure 2 - Historical material: A Zospeum isselianum syntype (MZUT M3232): upper mountains of Natisone River valley B–F Zospeum isselianum topotype: Turjeva jama G–P Specimens labelled as "Zospeum alpestre" in the Bourguignat Collection (MHNG 7898/2) from "Cav[erne]. de Carniole".
Figure 17 from: Jochum A, Slapnik R, Klussmann-Kolb A, Páll-Gergely B, Kampschulte M, Martels G, Vrabec M, Nesselhauf C, Weigand AM (2015) Groping through the black box of variability: An integrative taxonomic and nomenclatural re-evaluation of Zospeum isselianum Pollonera, 1887 and allied species using new imaging technology (Nano-CT, SEM), conchological, histological and molecular data (Ellobioidea, Carychiidae). Subterranean Biology 16: 123-165. https://doi.org/10.3897/subtbiol.16.5758
Figure 17 - Light micrograph of a section of the reproductive tract of Zospeum sp. (Konečka zijalka) (CSR SASA 21675). A Gonad (g) with acinus of the ovotestis including the germinal epithelial ring (ger) surrounding the acinar lumina (al), sustentacular cells (Sertoli cells) (sc) and oogonia (og) B Digestive gland (dg), hermaphroditic duct (hd) lined with cilia (c), posterior loop of the intestine (pi), receptaculum seminis (rs) containing allospermatozoa with spermatozoa oriented towards and apical ends touching the epithelial cells, and long motile cilia (ce) of intestine (i).
Figure 16 from: Jochum A, Slapnik R, Klussmann-Kolb A, Páll-Gergely B, Kampschulte M, Martels G, Vrabec M, Nesselhauf C, Weigand AM (2015) Groping through the black box of variability: An integrative taxonomic and nomenclatural re-evaluation of Zospeum isselianum Pollonera, 1887 and allied species using new imaging technology (Nano-CT, SEM), conchological, histological and molecular data (Ellobioidea, Carychiidae). Subterranean Biology 16: 123-165. https://doi.org/10.3897/subtbiol.16.5758
Figure 16 - Light micrograph of the histological appearance of the digestive tract of Zospeum sp. (Konečka zijalka) (CSR SASA 21675). A−B Bilobed muscular gizzard (mg) with epithelium showing columnar cells (cc) with microvillous apical surfaces (mv), posterior loop of the intestine (pi) with ciliated epithelium (ce) B Elaborate muscular plates (mp), thick cuticle (cu), highly ciliated rectum (r), gastric pouch (gp), digestive gland (dg) and hermaphroditic duct.
Figure 14 from: Jochum A, Slapnik R, Klussmann-Kolb A, Páll-Gergely B, Kampschulte M, Martels G, Vrabec M, Nesselhauf C, Weigand AM (2015) Groping through the black box of variability: An integrative taxonomic and nomenclatural re-evaluation of Zospeum isselianum Pollonera, 1887 and allied species using new imaging technology (Nano-CT, SEM), conchological, histological and molecular data (Ellobioidea, Carychiidae). Subterranean Biology 16: 123-165. https://doi.org/10.3897/subtbiol.16.5758
Figure 14 - Images of the radula of Zospeum isselianum (Turjeva jama) (CSR SASA 37013 conspecific) and Zospeum sp. (Konečka zijalka) (CSR SASA 21675). A−D SEM of the radular ribbon of topotypic Zospeum isselianum A Radular ribbon showing adhesive layer of contact zone with odontophore B SEM overview showing rachidian teeth (r) and lateral teeth (l) C Rows of lateral teeth bearing fine medial groove D Marginal teeth E Light micrograph showing the histological appearance of the radular sheath of Zospeum sp. (Konečka zijalka) showing the inferior epithelium (ie) attached to the radular membrane (rm) mineralizing cells (mc) with degenerating nuclei and radular teeth (rt).
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.