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469 results for “Image analysis”
Retinal status analysis method based on feature extraction and quantitative grading in OCT images
<p>The raw database includes 200 retinal OCT images judged as normal by ophthalmologists and 100 images with various abnormalities. The software includes the main steps for retinal status analysis. The software was carried out in Matlab.</p>
High resolution images for 'Identification of "BRAF-positive" cases based on whole-slide image analysis'
<p>This archive contains high resolution images to accompany the article 'Identification of “BRAF-positive” cases based on whole-slide image analysis' by V. Popovici, A. Krenek and E. Budinska.</p>
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>
Antibody panel used for multiplexed antibody-based imaging of Human Pancreas Analysis Program (HPAP) samples by CODEX
<p>This data file details antibodies applied to human pancreas tissue samples from the Human Pancreas Analysis Program (HPAP; RRID:SCR_016202) of the <a href="https://hirnetwork.org/">Human Islet Research Network</a> (HIRN; RRID:SCR_014393). Images will be uploaded for interactive analysis on <a href="https://pancreatlas.org/datasets">Pancreatlas</a> (RRID:SCR_018567) and made available for download via <a href="https://hpap.pmacs.upenn.edu/">PANC-DB</a>. Workflow is documented on protocols.io: <a href="https://dx.doi.org/10.17504/protocols.io.36wgq7dryvk5/v1">dx.doi.org/10.17504/protocols.io.36wgq7dryvk5/v1</a>.</p><p>Table format adapted from Radtke AJ, Quardokus EM, Saunders DC (2022), <a href="https://doi.org/10.5281/zenodo.7386417">SOP: Construction of Organ Mapping Antibody Panels for Multiplexed Antibody-Based Imaging of Human Tissues</a>. See also: Saunders D; Reihsmann R. <a href="https://doi.org/10.48539/HBM754.BHVR.258">OMAP-13: Organ Mapping Antibody Panel (OMAP) for Multiplexed Antibody-Based Imaging of Human Pancreas with CODEX, v1.0</a>.</p>
Multiplexed imaging analysis of human pancreatic islets from donors with and without type 2 diabetes
<p>This record contains tabular data from traditional and multiplexed immunohistochemistry experiments presented in the manuscript <i>Genetic risk converges on regulatory networks mediating early type 2 diabetes</i> (<a href="https://doi.org/10.1038/s41586-023-06693-2">Walker, Saunders & Rai et al., <i>Nature</i> 2023</a>), a body of work that includes tissue imaging, <a href="https://theparkerlab.shinyapps.io/Islet-RNAseq-WGCNA/">sorted islet cell transcriptomics</a>, and islet functional analysis of donors with early-stage type 2 diabetes (T2D) and control donors. Images can be viewed interactively on Pancreatlas (RRID:SCR_018567): <a href="https://pancreatlas.org/datasets/904/explore">https://pancreatlas.org/datasets/904/explore</a>.</p><p>All immunohistochemistry was performed on lightly PFA-fixed human pancreatic tissue (sample characteristics available in Supplementary Table 1). For traditional immunohistochemistry, islets were imaged at 20× with 2× digital zoom using a FV3000 confocal laser scanning microscope (Olympus) or full cross-sections were scanned on a ScanScope FL (Leica/Aperio). Quantitative analysis was carried out using HALO™ (Indica Labs) or Metamorph (Molecular Devices) software. For multiplexed immunohistochemistry, images were acquired using the PhenoCycler (CODEX) Open system (Akoya Biosciences) integrated with a BZ-X810 epifluorescence microscope (Keyence) with a CFI plan Apo I 20x/0.75 objective (Nikon). Image alignment, stitching, background subtraction, and deconvolution were performed using the CODEX Processor v1.7.0.6 (Akoya Biosciences). Cell segmentation and cell type annotations were generated using the HALO HighPlex FL v3.2.1 module (Indica Labs). For cell neighborhood (CN) analysis, two methods were applied in parallel to CODEX data from annotated islets: a community detection method, termed <i>Dynamic CF-IDF</i>, and a <i>k</i>-means approach. Packages used for cell neighborhood analyses are published in <a href="http://github.com/liu-bioinfo-lab/Cellular-Neighborhood-Analysis">Github</a>.</p>
Multiplexed imaging mass cytometry analysis characterizes the vascular niche in pancreatic cancer
<p>All data supporting the publication: "Multiplexed imaging mass cytometry analysis characterizes the vascular niche in pancreatic cancer."</p><p>1. Fully_Processed_OME.TIFF: This folder contains the OME.TIFF files with all markers after compensation and hot pixel removal for visualization of the data. These can be opened with QuPath and other software. </p><p>2. PDAC_IMC_Seurat_FINAL.rds: Seurat object of all cells included in the analysis with cell type and neighborhood annotations, and unintegrated and rPCA-integrated UMAP reductions. </p><p>3. Raw_Data_TIFF_Files: All raw individual TIFF files from the image acquisition</p><p>4. ROI_Selection: Brightfield and IHC images of individual samples showing where the ROIs for each sample are collected </p><p>5. Segmentation_Files: All relevant segmentation files from Mesmer for nuclear and whole cell segmentation. </p><p>6. H&E Images for each case scanned at 40x </p>
Thermal conductivity analysis of polymer-derived nano-composite via image-base structure reconstruction, computational homogenization and machine learning
<p>This dataset includes supplementary data and utilities for validating simulation results and training machine learning models as outlined in the publication titled "Thermal Conductivity Analysis of Polymer-Derived Nanocomposite via Image-Based Structure Reconstruction, Computational Homogenization, and Machine Learning" (<a href="https://doi.org/10.1002/adem.202302021">Fathidoost, 2024</a>).</p> <p>This dataset containes the microstructure images (identified by particle diameters size \(D_1\) and \(D_2\) volume fraction \(V_\mathrm{f}\) and aspect ratio \(A_\mathrm{r}\)) (see Table 1) and their corresponding homogenized thermal conductivity. these images resemble the microstructure of the monolithic \(\mathrm{(Hf,Ta)C/SiC}\) ceramic following FAST sintering, the material system of this work (<a href="https://doi.org/10.1002/adem.202302021">Fathidoost, 2024</a>). White and black colors within the images represent distinct regions of the material system, respectively referring to former powder particles (FPPs) and sinter necks (SNs), which is explained in this work.</p> <p>Table 1. Parameterized descriptors extracted from the mesoscale SEM image analysis</p> <table> <tbody> <tr> <td>Param.</td> <td>Mean [unit]</td> <td>Std.</td> </tr> <tr> <td>\(D_{1}\)</td> <td>40, 50, 60 [μm]</td> <td>20%</td> </tr> <tr> <td>\(D_{2}\)</td> <td>20, 25, 26, 30, 33, 40 [μm]</td> <td>30%</td> </tr> <tr> <td>\(V_\mathrm{f}\)</td> <td>1.5, 2.0</td> <td>-</td> </tr> <tr> <td>\(A_\mathrm{r}\)</td> <td>35, 40, 45, 55, 60 [%]</td> <td>-</td> </tr> </tbody> </table> <p>This dataset contains:</p> <ul> <li><em>dataset.csv: </em>containing a summary of data including the names of microstructure images, their corresponding geometric details, as well as the first and third principal components of two-point statistics for all images, along with the effective thermal conductivity of the corresponding microstructures. Further details can be found in the associated publication.</li> <li><em>microstructures_images.zip</em>: containing binary cross-section images of the RVEs from synthetic microstructures。</li> <li><em>results.zip:</em> contains all the simulation results based on digitized diffuse-interface microstructures, which can be opened by the post-processing software, such as ParaView.</li> </ul>
Data for "Laundry to Laboratory: Automated Image Analysis for the Characterization of Fibrous Microplastics"
<p>This repository contains a representative subset of filter paper images used to evaluate the various experimental conditions in the manuscript "Laundry to Laboratory: Automated Image Analysis for the Characterization of Fibrous Microplastics." </p>
Supplementary Materials to Curvature analysis in seed surface of SEM images of Silene species from Turkey
<p><span>(1) JPG files with seed images, Bézier curves and curvature analysis for 40 seeds (one seed of each species); (2) Mathematica files with curvature analysis corresponding to 40 seeds (one seed of each species).</span></p>
Image datasets associated with Gut Analysis Toolbox
<div>The images are sample image datasets associated with the software: <a href="https://gut-analysis-toolbox.gitbook.io/docs/">Gut Analysis Toolbox (GAT)</a>.</div> <div>The dataset contains immunofluorescence images of enteric neurons and glia labeled with different markers. The data is mostly from mouse and human colon or small intestine. </div> <div>Channels corresponding to Hu labelling can be used for segmenting enteric neurons in GAT. </div> <div>Channels corresponding to GFAP (enteric glia) or neurons with markers labelling the cell body and processes (ChAT, Calbindin, Calretinin) can be used as a ganglia marker for segmenting the ganglia</div> <div>The data is two-dimensional (2D) with some images having multiple channels. The data is mostly in tif format, except for one dataset that is czi (Fiji using bioformats or aicspylibczi in Python). Calcium imaging data is 2D+Time.</div> <div> </div> <div>Data curated by: <a href="https://www.linkedin.com/in/rajapradeep/">Pradeep Rajasekhar, Walter and Eliza Hall Institute of Medical Research</a>, Australia</div> <h2><strong>Data from<a href="https://www.monash.edu/mips/themes/drug-discovery-biology/labs/inm"> INM lab, Monash University</a> (mouse images)</strong></h2> <p><strong>Immunofluorescence images</strong></p> <ul> <li> 181107_ms_distal_colon_GFAP_Hu_40X.tif <ul> <li>Channel 1: GFAP</li> <li>Channel 2: Hu</li> </ul> </li> </ul> <div> <ul> <li>181107_ms_distal_colon_nNOS_GFAP_Hu_40X.tif (Same as above, but got an extra channel)</li> </ul> </div> <ul> <li> <ul> <li>Channel 1: nNOS</li> <li>Channel 2: GFAP</li> <li>Channel 3: Hu</li> </ul> </li> </ul> <div>In both images above, GFAP can be used as ganglia segmentation channel in GAT using DeepImageJ.</div> <div> <ul> <li>ms_distal_colon_Hu_20X.tif</li> </ul> </div> <div> <ul> <li> <ul> <li>Hu</li> </ul> </li> </ul> </div> <div> <ul> <li>ms_distal_colon_Hu_40X_1.tif</li> </ul> </div> <div> <ul> <li> <ul> <li>Hu</li> </ul> </li> </ul> </div> <div> <ul> <li>Tilescan_GAT_ms_distal_colon_MP_hu.tif</li> </ul> </div> <div> <ul> <li> <ul> <li>Hu</li> </ul> </li> </ul> </div> <h3><strong>Calcium imaging data (video)</strong></h3> <div>• calcium_imaging_mouse_distal_colon_25X.tif</div> <div>Tissue was incubated with calcium dye Fluo8-AM calcium dye (Myenteric wholemount from distal colon of mouse)</div> <div>142 frames in total acquired at 1.162 frames per second. At frame 52, 100 uM of ATP is added which causes enteric glia, neurons and blood vessels to respond. This causes slow drifting in the field of view.</div> <div> </div> <div>• mouse_GCamp_calcium_movement.tif</div> <div> </div> <div>Wnt1-GCaMP3 mouse where GCamP3 is a genetically encoded calcium sensor expressed by enteric neurons and enteric glia</div> <div>Imaging performed at Monash University. 745 frames in total acquired at 1.162 frames per second with drifting over time</div> <div>Tissue source: <a href="https://biomedicalsciences.unimelb.edu.au/sbs-research-groups/anatomy-and-physiology-research/neuroscience/development-of-the-enteric-nervous-system">Stamp & Hao laboratory, University of Melbourne.</a></div> <div> </div> <h2><strong>Images from McQuade Lab, University of Melbourne</strong></h2> <ul> <li>ms_28_wk_colon_DAPI_nNOS_Hu_10X.tif (mouse colon)</li> <li>ms_28_wk_colon_DAPI_nNOS_Hu_10X.tif (mouse ileum) <ul> <li>Channel 1: DAPI</li> <li>Channel 2: nNOS </li> <li>Channel 3: Hu</li> </ul> </li> </ul> <div> <ul> <li>ms_distal_colon_nNOS_Hu_10X.czi (This is a .czi file which can be opened in Fiji using bioformats or aicspylibczi in Python)</li> </ul> </div> <ul> <li> <ul> <li>Channel 1: DAPI</li> <li>Channel 2: nNOS </li> <li>Channel 3: Hu</li> </ul> </li> </ul> <h2><strong>Images from public repository (SPARC)</strong>:</h2> <ul> <li>DYM_22_7_Pr_Chat_BYFP_DIN_GFP-g_nNOS-m_VIP-r_Hu-b.tif is a crop from File 100 05-07-2019 DYM 22 7 Pr Chat%3BYFP DIN GFP-g nNOS-m VIP-r Hu-b (Mouse Proximal Colon)</li> <li>DYM_22_7_Pr_Hu_crop.tif is a crop from above. <ul> <li>Channel 1: Choline acetyltransferase</li> <li>Channel 2: nNOS</li> <li>Channel 3: Calretinin</li> <li>Channel 4: Hu (pan-neuronal marker)</li> </ul> </li> </ul> <div> Channel 1 and 3 can be used as ganglia segmentation channels in GAT using DeepImageJ.</div> <div> </div> <div> <ul> <li>146_02_14_20DYM8_6_mouse_Mid_Chat-g CalB-r CalR-b_max.tif is from File 146 02-14-20 DYM 8 6 Mid Chat-g CalB-r CalR-b (Mouse mid colon)</li> </ul> </div> <div> No Hu staining, any channel could be used as ganglia segmentation channel in GAT using DeepImageJ.</div> <div> <ul> <li> <ul> <li>Channel 1: ChAT</li> <li>Channel 2: Calbindin</li> <li>Channel 3: Calretinin</li> </ul> </li> </ul> </div> <h3><strong>Reference</strong>:</h3> <div>Thanks goes to Marthe Howard for depositing the data in the SPARC repository.</div> <div>Howard, M. (2021). 3D imaging of enteric neurons in mouse (Version 1) [Data set]. SPARC Consortium.<a href="https://doi.org/10.26275/9FFG-482D"> https://doi.org/10.26275/9FFG-482D</a></div> <div>**************</div> <h2><strong>Multiplex data (Flinders University)</strong></h2> <div><strong>Multiplexing_H2202Desc_Layer 1_Ganglia1_Hu.zip </strong>is from:</div> <div><a href="https://pubmed.ncbi.nlm.nih.gov/37355216/">Chen, B. N., Humenick, A., Yew, W. P., Peterson, R. A., Wiklendt, L., Dinning, P. G., Spencer, N. J., Wattchow, D. A., Costa, M., & Brookes, S. J. H. (2023). Types of Neurons in the Human Colonic Myenteric Plexus Identified by Multilayer Immunohistochemical Coding. Cellular and molecular gastroenterology and hepatology, 16(4), 573–605.</a></div> <div>This data is a myenteric wholemount from the descending colon of a Human. It has 14 different markers, 6 different rounds of staining. Every round has pan-neuronal marker Hu as a reference marker.There are 19 images. The filenames follow the convention:</div> <div> </div> <div>H2202Desc_<em>layer num</em>_<em>ganglia num</em>_<em>markername</em>.tif </div> <div><em>H2202 </em>is the sample name, <em>Desc </em>means descending colon</div> <div>Here <em>layer num</em> corresponds to the round of staining, so Layer3, means its the 3rd round of staining.</div> <div><em>ganglia num</em> is specified as multiple ganglia can be imaged from same tissue. </div> <div><em>markername</em> corresponds to the marker used. </div> <div> </div> <div>Markers used are: Hu, 5HT, ChAT, NOS, CGRP, Enk, SP, Somat, VACht, NPY, Calbindin, Calretinin, NF, VIP</div> <div> </div> <div><strong>Abbreviations:</strong></div> <div> </div> <ul> <li>Hu: Pan-neuronal marker</li> <li>5HT: Serotonin (5-Hydroxytryptamine)</li> <li>ChAT: Choline acetyltransferase</li> <li>nNOS: neuronal Nitric Oxide Synthase (NOS in these images are actually nNOS)</li> <li>CGRP: Calcitonin Gene-Related Peptide</li> <li>Enk: Enkephalin</li> <li>SP: Substance P</li> <li>Somat: Somatostatin</li> <li>VACht: Vasoactive Intestinal Peptide (VIP) </li> <li>NPY: Neuropeptide Y</li> <li>NF: neurofilament 200 </li> </ul>
BEHAV3D: A 3D live imaging platform for comprehensive analysis of engineered T cell behavior and tumor response
<p>The use of patient-derived material and immune cell co-cultures in modeling immune-oncology has gained significant interest for understanding and manipulating immune cell tumor targeting in a patient-specific context. However, current protocols have limitations in visualizing and analyzing the dynamic cellular features of these living culture systems. We recently developped a workflow names BEHAV3D, that combines multi-color live 3D imaging and computational tools to analyze cell death dynamics, classify T cell behavior, and generate data-informed 3D images and videos. Here we provide some example pre-processed dataset of videos of two co-culture set ups: breast cancer Patient Derived Organoids with αβ T cells engineered to express a γδ TCR (TEGs) and Acute Lymphoblastic Leukemia cells with CD19 CART cells.</p>
msiFlow: Automated Workflows for Reproducible and Scalable Multimodal Mass Spectrometry Imaging and Immunofluorescence Microscopy Data Processing and Analysis
<p>This record contains example and result data of msiFlow.</p> <p>msiFlow is a collection of automated workflows for reproducible and scalable multimodal mass spectrometry imaging (MSI) and immunofluorescence microscopy (IFM) data processing and analysis. Using an experimental mouse model for urinary tract infection, induced by uropathogenic E.coli (UPEC), we generated data by</p> <ul> <li>matrix-assisted laser desorption ionisation mass spectrometry imaging with laser-induced postionisation (MALDI-2 MSI) using the Bruker timsTOFfleX instrument</li> <li>transmission-mode MALDI-2 MSI (t-MALDI-2)</li> <li>immunofluorescence microscopy (IFM) using the MACSima system from Miltenyi </li> </ul> <p>msiFlow was tested on MALDI-2 MSI, t-MALDI-2 MSI and IFM data of control and UPEC-infected mouse bladder sections. In IFM we used Ly6G and actin for staining neutrophils and the muscle layer. We validated msiFlow on MALDI MSI data of bone marrow (BM)-derived neutrophils. Tentative lipid annotations were validated by MALDI DDA MSI and MALDI MS/MS. All data used and results generated by msiFlow are included in this dataset (besides the intermediate results of the MALDI-2 preprocessing due to data size).</p> <p>The dataset contains the following zip files:</p> <table> <tbody> <tr> <td><strong>zip file</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>ly6g_heterogeneity.zip</td> <td>example and result data (Ly6G clusters) for molecular_heterogeneity_flow</td> </tr> <tr> <td>if_segmentation.zip</td> <td>example and result data (Ly6G segmentation) for if_segmentation_flow</td> </tr> <tr> <td>ly6g_heterogeneity_signatures.zip</td> <td>example and result data (lipids for Ly6G clusters) for molecular_signatures_flow</td> </tr> <tr> <td>ly6g_molecular_signatures.zip</td> <td>example and result data (lipids for Ly6G) for molecular_signatures_flow</td> </tr> <tr> <td>msi_if_registration.zip</td> <td>example and result data for msi_if_registration_flow</td> </tr> <tr> <td>msi_segmentation.zip</td> <td>example and result data (segmented MSI bladder data) for msi_segmentation_flow</td> </tr> <tr> <td>region_group_analysis.zip</td> <td>example and result data (regulated lipids in different bladder tissue regions) for region_group_analysis_flow</td> </tr> <tr> <td>macsima.zip</td> <td>raw IFM data of UPEC-infected bladders containing Ly6G, actin and autofluorescence images</td> </tr> <tr> <td>maldi-bm-neutrophils.zip</td> <td>raw and pre-processed MALDI MSI data of BM-derived neutrophils</td> </tr> <tr> <td>t-maldi-2.zip</td> <td>raw t-MALDI-2 MSI data of a UPEC-infected bladder section</td> </tr> <tr> <td>maldi-2-<em>group-sampleno</em>.zip</td> <td>raw MALDI-2 MSI data of a control/UPEC bladder section</td> </tr> <tr> <td>MALDI_DDA_MSI.zip</td> <td>raw MALDI MSI data acquired in DDA mode</td> </tr> <tr> <td>TIMS_MS_MS.zip</td> <td>raw MALDI TIMS MS/MS data</td> </tr> </tbody> </table> <p> </p>
PathoEye: a deep learning framework for histopathological image analysis of skin tissue
<p>This dataset comprises two parts: one consisting of skin pathology slices near the epidermal layer of elderly individuals under sunlight exposure conditions, and the other comprising skin pathology slices near the epidermal layer of young individuals under the same conditions. Each type of image comes in two sizes: one is 128×128 pixels, and the other is 512×512 pixels.</p>
MRI images and code for data analysis
<p>Author: Samuele Ceolin</p> <p>Luxembourg Institute of Science and Technology, Environmental Research and Innovation Department</p> <p>Correspondence: samuele.ceolin94@gmail.com</p> <p>Copyright © 2019-2024 Luxembourg Institute of Science and Technology. All Rights Reserved.</p> <p>The code in this repository is distributed under the terms of the <a href="https://www.gnu.org/licenses/gpl-3.0.html" target="_blank" rel="nofollow noreferrer noopener">GNU General Public License 3.0</a> or any later version, unless stated otherwise. All other material is licensed under a <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" rel="nofollow noreferrer noopener">Creative Commons Attribution 4.0 International license</a>, unless stated otherwise.</p> <p><strong>General purpose</strong></p> <p>Repository containing the MRI data and images (MRI_data.zip) and containing the code for the extrapolation of root properties and data analysis (MRI_code.zip).</p> <p>See the file README.md contained in "MRI_code.zip" for more information.</p>
Data, code, and manual for analysis of image heterogeneity in mass spectrometric imaging.
<p>This upload contains all replication material for "The software for interactive evaluation of mass spectrometric imaging heterogeneity" (forthcoming).</p> <p><strong>Authors:</strong> E.S. Zhvansky, E.V. Zhdanova, M.S. Belenikin, M.A. Shamraeva, S.V. Silkin, K.V. Bocharov, A.A. Sorokin.</p> <p><strong>Code, manual, and data are located within Interactive_CSMM.zip</strong><strong>.</strong> Code is written MATLAB R2019b and Python 3.5.2.</p> <p>Please find the README.md for code using and the code to replicate the main findings of the paper described below:</p> <ul> <li>imzml2mat.py in conversion directory for file conversion.</li> <li>CSMM.m for application start.</li> <li>40TopL,10TopR,30BottomL,20BottomR-centroid.mat - data in conversion/Data directory for reproducing the results.</li> <li>User_manual.pdf - manual.</li> <li>mp4 files - screencasts.</li> </ul>
Analysis of the root diameter distribution from time series images of real and simulated Cassava root systems
<p>The data was collected, simulated and analyzed in the framework of the CassavaStore project (a collaboration between IBG-2, Forschungszentrum Jülich, Germany and different institution from Thailand; for details see <a href="https://www.international-bioeconomy.org/cassavastore_eng">https://www.international-bioeconomy.org/cassavastore_eng</a>). Aim of this project is to get a better understanding of storage root development in cassava (<em>Manihot esculenta</em> Crantz) in order to optimize cassava growth with respect to variety breeding and growth management. The storage root is one of the main providers of starch in Thailand and therefore of high economic importance. Monitoring the formation of storage roots over time via quantification of the root diameter distribution of excavated root systems was one of the key aspects addressed in this project. To measure the diameters a software was developed that identifies roots in RGB images and analyzes the diameters along each identified root automatically. The published data contains 1) analyzed images from cassava roots that were acquired in a video box; 2) simulated virtual root model images with known root diameter distributions that were used to validate the analysis approach; 3) a description of the data and the folder structure.</p>
Bayesian machine learning analysis of single-molecule fluorescence colocalization images
<p>Data files for the "Bayesian machine learning analysis of single-molecule fluorescence colocalization images" manuscript.</p>
Analysis on Multimodal Imaging of 'stealth' Choroidal Neovascularization in Central Serous Chorioretinopathy
<p><strong>Purpose:</strong> To investigate the prevalence of concurrent choroidal neovascularization (CNV) in patients with clinically diagnosed of central serous chorioretinopathy (CSC) and analyze the multimodal imaging characteristics of CSC with ‘stealth’ CNV (CSC-sCNV). <strong>Design: </strong>Retrospective, observational cross-sectional study. <strong>Methods</strong><strong>:</strong> The current study analyzed the clinical data of 111 clinically diagnosed of CSC patients (134 eyes) who received fluorescein fundus angiography (FFA), indocyanine green angiography (ICGA), and optical coherence tomography angiography (OCTA) at their initial visit. The CSC-CNV that can be detected by FFA+ICG was defined as classic CSC-CNV (CSC-cCNV), and CSC-CNV that cannot be detected by FFA+ICG but can be detected by OCTA was defined as CSC-sCNV. The multimodal imaging characteristics of CSC-sCNV were analyzed.<strong> Results: </strong>The concurrent CNV was found in 29 eyes (21.6%) of 28 patients (25.2%), who were significantly older than those without CNV (P=0.039). Among the 29 eyes, 6 eyes (20.7%) were CSC-cCNV and 23 (79.3%) were CSC-sCNV. All CSC-sCNV eyes were type 1 CNV, of which, 22 had photoreceptor layer defects of varying severities. OCTA showed 4 (17.4%) were active CNV and 19 (82.6%) were inactive CNV based on the morphology features. CSC-sCNV lesions manifested as smoke-stack leakage, diffuse RPE changes with/without ink-blot leakage and normal fluorescence on FFA (1 eye, 7 eyes, 10 eyes and 5 eyes respectively). ICGA showed no typical CNV features in CSC-sCNV but choroidal vasodilation beneath CNV lesions and choroidal hyperpermeability at the posterior pole.<strong> Conclusion:</strong> CSC-sCNV may be the primary phenotype of CSC-CNV and its inactivity might be the reason that it cannot be detected by FFA.</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>
THE WHOLE IS OTHER THAN THE SUM OF ITS PARTS: SENSIBILITY ANALYSIS OF 360° URBAN IMAGE SPLITTING
<p>Code and data for the paper THE WHOLE IS OTHER THAN THE SUM OF ITS PARTS: SENSIBILITY ANALYSIS OF 360° URBAN IMAGE SPLITTING - B. Beaucamp, T. Leduc, V. Tourre, M. Servières</p>
ScienceDex guides
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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.