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21,179 results for “image enhancement”
Manganese Enhanced Magnetic Resonance Imaging reveals light-induced brain asymmetry in embryo
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Datasets, models and demos associated to "Celldetective: an AI-enhanced image analysis tool for unraveling dynamic cell interactions"
<p>This repository contains datasets, models and demos associated to <a href="https://github.com/remyeltorro/celldetective">Celldetective</a>, a software for single-cell analysis from multimodal time lapse microscopy images. </p> <h1>Demos</h1> <h2>Cell-cell interaction assay: ADCC</h2> <p>We imaged a co-culture of MCF-7 breast cancer cells (targets) and human primary NK cells (effectors), interacting in the presence of bispecific antibodies, to measure antibody dependent cellular cytotoxicity (ADCC). The nuclei of all cells are marked with the Hoechst nuclear stain, the dead nuclei with the propidium iodide nuclear stain, the cytoplasm of the NK cells with CFSE. The system in epifluorescence and brightfield at either 20 or 40X magnification. We provide a single position demo for the ADCC assay, as "demo_adcc.zip". After unzipping, the demo_adcc folder can be loaded in Celldetective for testing. </p> <h2>Cell-surface interaction assay: RICM</h2> <p>We imaged human primary NK cells engaging in spreading with a surface coated with a bispecific antibody similar to the one used in the ADCC assay (replacing the target cells with a flat surface). The system is imaged using the RICM technique. Images are normalized using a median estimate of the background, pooled from all the positions in a well and dividing the images by this estimate. Here, we provide a single position demo for the cell-surface interactiona assay imaged in RICM, as "demo_ricm.zip". As above, after unzipping, the experiment can be tested and processed in Celldetective.</p> <h1>Datasets</h1> <h2>Image annotations for segmentation</h2> <h3>Cell-cell interaction assay: ADCC</h3> <p>We generated two sets of annotations from images of a co-culture of MCF-7 breast cancer cells and human primary NK cells, interacting in the presence of bispecific antibodies, to measure antibody dependent cellular cytotoxicity (ADCC). Since there are two separate cell populations of interest, the targets (MCF-7) and effectors (NK cells), we curated two datasets. Each sample in a dataset consists of a multichannel image (up to five channels in the context of ADCC, among brightfield , Hoechst nuclear stain, PI nuclear stain, CFSE, LAMP1), the associated instance segmentation annotation for the population of interest and a json file summarizing the content of each channel and the spatial calibration of the image. These sample data are generated directly in Celldetective, using a custom napari plugin.</p> <ul> <li>db_mcf7_nuclei_w_lymphocytes: MCF-7 cell nuclei are annotated specifically on images where primary NK cells (or rarely primary T cells), and RBCs co-exist. The annotation exploits up to four channels simultaneously.</li> <li>db_primary_NK_w_mcf7: human primary NK cells, with annotated cytoplasm (mostly from CFSE) but exploiting brightfield and Hoechst to segment out of focus or poorly labelled cells.</li> </ul> <p>These datasets are used to train several segmentation models to segment on one hand the MCF-7 nuclei and on the other hand the primary NK cells.</p> <h3>Cell-surface interaction assay: RICM</h3> <ul> <li>db_spreading_lymphocytes: we provide a dataset of primary NK cells (and occasionnaly mice T cells) imaged in RICM (with sometimes paired brightfield images). Cells are detected as soon as they start forming interferences on the image (hovering behavior). A pre-annotation was performed using a threshold based segmentation on the RICM modality. Manuel separation of cell-cell contacts and removal of false positive objects was performed by an expert annotator (using brightfield when available). RBCs are ignored in the annotations. </li> </ul> <h2>Single-cell signal annotations for classification and regression</h2> <h3>Cell-cell interaction assay: ADCC</h3> <p>We generated several signal classification/regression datasets with Celldetective to characterize the ADCC assay. Briefly, for a given event cells can be classified as "the event occured during the observation", "no event occured during the observation", "the event already occured prior to observation". If the event occurred during the observation, we can estimate when (the regression). Each single-cell is a dictionary with a collection of signals. The attribute "class" sets the class and "t0" the time of event (default is -1 for absence of event). </p> <ul> <li>db-si-NucPI: classification and regression of single-cells with respect to lysis events characterized by a strong PI increase upon lysis (also associated with decreasing nuclear area and sometimes a decreasing Hoechst)</li> <li>db-si-NucCondensation: classification and regression of single-cells with respect to nucleus shrinking events characterized by a decreasing nuclear area (UPDATE on 23/01/2024)</li> </ul> <h1>Models</h1> <h2>Segmentation models</h2> <h3>Generalist models</h3> <p>We integrated in Celldetective select published models for cellular segmentation from StarDist and Cellpose. We wraped the models with an input configuration to help Celldetective handle the normalization, rescaling and channel selection upon inference. </p> <ul> <li>Cellpose [1,2]: <em>cyto3</em>, <em>livecell</em>, <em>tissuenet</em>, <em>nuclei</em></li> <li>StarDist [3]: <em>versatile_fluo</em>, <em>versatile_he</em></li> </ul> <p>If you use any of these models your research, don't forget to cite the StarDist or Cellpose papers accordingly!</p> <h3>ADCC models</h3> <ul> <li>MCF-7 (in the presence of lymphocytes): <em>mcf7_nuc_multimodal, mcf7_nuc_stardist_transfer</em></li> <li>primary NKs (in the presence of MCF-7): <em>primNK_multimodal</em>, <em>primNK_SD</em>, <em>primNK_cfse</em></li> </ul> <h3>Spreading-assay models</h3> <ul> <li>Lymphocytes: <em>lymphocytes_ricm</em></li> </ul> <h2>Signal analysis models</h2> <p>We developed Deep Learning models that classify and regress the time of events from single-cell signals, applied to the ADCC assay.</p> <ul> <li> lysis detection: <em>lysis_H_PI</em>, <em>lysis_PI_area</em><em>. </em>Detect lysis events characterized at least by an increase of PI from one or more measurements (respectively PI+Hoechst and PI+nucleus area, trained on db-si-NucPI)</li> <li>nucleus shrinking detection:<em> NucCond</em>. Detect nucleus shrinking events from nuclear area signal (db-si-NucCondensation)</li> </ul> <h1>References</h1> <ol> <li>Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18, 100–106 (2021).</li> <li>Pachitariu, M. & Stringer, C. Cellpose 2.0: how to train your own model. Nat Methods 19, 1634–1641 (2022).</li> <li>Schmidt, U., Weigert, M., Broaddus, C. & Myers, G. Cell Detection with Star-Convex Polygons. in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 (eds. Frangi, A. F., Schnabel, J. A., Davatzikos, C., Alberola-López, C. & Fichtinger, G.) 265–273 (Springer International Publishing, Cham, 2018). doi:10.1007/978-3-030-00934-2_30.</li> </ol> <p> </p> <p> </p>
Image Enhancement with Fractional Calculus
<p>The video included in this repository shows some examples of image enhancement by exploiting fractioal calculus. The input images (showed on the left part of the video) are those of the dataset BSIMAGE downloadable from https://zenodo.org/records/13903974. The output image (on the right part of the video) are obtained by an algorithm exploiting both integer- and fractional-order image gradients, described in the paper cited below.</p>
Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to detect breast malignant Lesions
<p>We uploade the dataset of the manuscript "Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to detect breast malignant Lesions" by Current Oncology.</p> <p> </p>
Correlative Raman Imaging and Scanning Electron Microscopy: The Role of Single Ga Islands in Surface-Enhanced Raman Spectroscopy of Graphene_experimental dataset
<p>This dataset contains the raw unprocessed data for Piastek et al., Correlative Raman Imaging and Scanning Electron Microscopy: The Role of Single Ga Islands in Surface-Enhanced Raman Spectroscopy of Graphene, <em>J. Phys. Chem. C</em> 2022, 126, 9, 4508–4514. </p>
Data pertaining to the published article "Detection of pathological contrast enhancement with synthetic brain imaging from quantitative multiparametric MRI" by Donatelli et al., 2024
<p>Data pertaining to the published article "Detection of pathological contrast enhancement with synthetic brain imaging from quantitative multiparametric MRI" by Donatelli et al., 2024. <a href="https://doi.org/10.1111/jon.13201">https://doi.org/10.1111/jon.13201</a></p>
Images and plotting code : Stray light correction and enhancement of nocturnal low-light image of early-morning-orbiting Fengyun-3E satellite
<p>These are images to demonstrate the effectiveness of the algorithm and plotting code : Stray light correction and enhancement of nocturnal low-light image of early-morning-orbiting Fengyun-3E satellite. Readme.pdf will provide specific instructions about these folder compression packages.</p>
MyoD1 localization at the nuclear periphery is mediated by association of WFS1 with active enhancers - Image Dataset
<p>The set of raw images used in this study published on Nature Communications:</p> <p><a href="https://www.nature.com/articles/s41467-025-57758-x" target="_blank" rel="noopener">https://www.nature.com/articles/s41467-025-57758-x</a></p> <p>are available as 4 subsets below:</p> <p><a href="https://osf.io/w5n43/" target="_blank" rel="noopener">https://osf.io/w5n43/</a></p> <p><a href="https://osf.io/bfxdw/" target="_blank" rel="noopener">https://osf.io/bfxdw/</a></p> <div><a href="https://osf.io/rkguq/" target="_blank" rel="noopener">https://osf.io/rkguq/</a></div> <div> </div> <div><a href="https://osf.io/q6b4j/" target="_blank" rel="noopener">https://osf.io/q6b4j/</a> <div> </div> <div>The reason of this Zenodo entry is to combine these subsets into a single DOI link.</div> <div>The total size adds up to 200GB and it is not possible to host such a large dataset on Zenodo, and it is only possible as maximum of 50GB pieces on Open Science Framework.</div> <div> </div> <div>Summary of the study:</div> <div>Spatial organization of the mammalian genome influences gene expression and cell identity. While association of genes with the nuclear periphery is commonly linked to transcriptional repression, also active, expressed genes can localize at the nuclear periphery. The transcriptionally active MyoD1 gene, a master regulator of myogenesis, exhibits peripheral localization in proliferating myoblasts, yet the underlying mechanisms remain elusive. Using a newly generated reporter cell line, we demonstrate here that peripheral association of the MyoD1 locus is independent of mechanisms involved in heterochromatin anchoring. We identify a set of nuclear envelope transmembrane proteins, particularly WFS1, that actively tether MyoD1 to the nuclear periphery. WFS1 primarily associates with active distal enhancer elements upstream of MyoD1, and with a subset of enhancers enriched in active histone marks genome-wide, which are linked to expressed myogenic genes. Overall, our data identify a novel mechanism involved in tethering active genes to the nuclear periphery.</div> <div> </div> <div>This research was funded in whole or in part by the Austrian Science Fund (FWF) [P29713-B28, P32512-B and P36503-B] to Roland Foisner and a doctorate program funded by the Austrian Science Fund (FWF) [W1261-B28].</div> <div> </div> </div>
Phenotyping Interstitial Cystitis/Bladder Pain Syndrome (IC/BPS) by Intravesical Contrast Enhanced - Magnetic Resonance Imaging (ICE-MRI) Bladder Permeability Assay
ClinicalTrials.gov study NCT05811377. IPD Sharing: NO. Countries: 1. Publications: 2.
Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Measuring Effects of Pazopanib Hydrochloride in Patients With Metastatic Kidney Cancer
ClinicalTrials.gov study NCT01599832. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Imaging of Osteonecrosis With Ferumoxytol-Enhanced MRI
ClinicalTrials.gov study NCT02893293. IPD Sharing: NO. Countries: 1. Publications: 3.
Exposure, D-cycloserine Enhancement, and Functional Magnetic Resonance Imaging (fMRI) in Snake Phobics
ClinicalTrials.gov study NCT01450306. IPD Sharing: YES. Countries: 1. Publications: 1.
Gadoxetate Enhanced Imaging Study to Detect Prostate Cancer
ClinicalTrials.gov study NCT01867424. IPD Sharing: NO. Countries: 1. Publications: 3.
Deep learning-based autofocus method enhances image quality in light-sheet fluorescence microscopy
Open the record for dataset details and reuse information.
Electrochemically controlled switching of dyes for enhanced super-resolution optical fluctuation imaging (EC-SOFI)
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LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Andrews Forest LTER collected on 1982-12-13
This LTER Remote Sensing spatial raster dataset consists of LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Andrews Forest LTER, originally collected on 1982-12-13 (18:24:53.0540560Z) by Landsat 4, row 29, path 46. Cloud cover was 50 percent. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software was originally developed by the National Aeronautics and Space Administration–Goddard Space Flight Center and the University of Maryland to produce top-of-atmosphere reflectance from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus Level 1 digital numbers and to apply atmospheric corrections to generate a surface-reflectance product. The U.S. Geological Survey (USGS) has adopted the LEDAPS algorithm for producing the Landsat Surface Reflectance Climate Data Record. NASA Landsat Program, 2009, Landsat TM LT40460291982347XXX01, LPGS_12.0.2, USGS, Sioux Falls, 2012-06-10T15:25:20Z.
LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Andrews Forest LTER collected on 1984-07-03
This LTER Remote Sensing spatial raster dataset consists of LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Andrews Forest LTER, originally collected on 1984-07-03 (18:24:58.7720880Z) by Landsat 5, row 29, path 46. Cloud cover was 0.1 percent. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software was originally developed by the National Aeronautics and Space Administration–Goddard Space Flight Center and the University of Maryland to produce top-of-atmosphere reflectance from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus Level 1 digital numbers and to apply atmospheric corrections to generate a surface-reflectance product. The U.S. Geological Survey (USGS) has adopted the LEDAPS algorithm for producing the Landsat Surface Reflectance Climate Data Record. NASA Landsat Program, 2009, Landsat TM LT50460291984185PAC00, LPGS_12.0.2, USGS, Sioux Falls, 2012-05-18T19:30:38Z.
LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Andrews Forest LTER collected on 1994-03-25
This LTER Remote Sensing spatial raster dataset consists of LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Andrews Forest LTER, originally collected on 1994-03-25 (18:16:54.8730190Z) by Landsat 5, row 29, path 46. Cloud cover was 0 percent. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software was originally developed by the National Aeronautics and Space Administration–Goddard Space Flight Center and the University of Maryland to produce top-of-atmosphere reflectance from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus Level 1 digital numbers and to apply atmospheric corrections to generate a surface-reflectance product. The U.S. Geological Survey (USGS) has adopted the LEDAPS algorithm for producing the Landsat Surface Reflectance Climate Data Record. NASA Landsat Program, 2009, Landsat TM LT50460291994084XXX02, LPGS_12.0.2, USGS, Sioux Falls, 2012-05-19T04:11:26Z.
LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Central Arizona - Phoenix LTER collected on 1992-04-30
This LTER Remote Sensing spatial raster dataset consists of LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Central Arizona - Phoenix LTER, originally collected on 1992-04-30 (17:21:15.6250190Z) by Landsat 5, row 36, path 36. Cloud cover was 30 percent. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software was originally developed by the National Aeronautics and Space Administration–Goddard Space Flight Center and the University of Maryland to produce top-of-atmosphere reflectance from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus Level 1 digital numbers and to apply atmospheric corrections to generate a surface-reflectance product. The U.S. Geological Survey (USGS) has adopted the LEDAPS algorithm for producing the Landsat Surface Reflectance Climate Data Record. NASA Landsat Program, 2009, Landsat TM LT50360361992121XXX02, LPGS_12.0.2, USGS, Sioux Falls, 2012-08-17T20:14:38Z.
LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Shortgrass Steppe collected on 1995-04-18
This LTER Remote Sensing spatial raster dataset consists of LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Shortgrass Steppe, originally collected on 1995-04-18 (16:46:05.7110690Z) by Landsat 5, row 032, path 033. Cloud cover was 10 percent. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software was originally developed by the National Aeronautics and Space Administration–Goddard Space Flight Center and the University of Maryland to produce top-of-atmosphere reflectance from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus Level 1 digital numbers and to apply atmospheric corrections to generate a surface-reflectance product. The U.S. Geological Survey (USGS) has adopted the LEDAPS algorithm for producing the Landsat Surface Reflectance Climate Data Record. NASA Landsat Program, 2009, Landsat TM LT50330321995108AAA01, LPGS_12.1.3, USGS, Sioux Falls, 2012-12-22T08:08:12Z.
ScienceDex guides
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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.