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Dataset results
26 results for “StarDist”
StarDist_TumorCell_nuclei
<p>This repository contains a StarDist deep learning model designed for segmenting tumor cell nuclei from the DAPI channel in fluorescence microscopy images while excluding HUVEC nuclei. The model was trained to accurately detect individual tumor cell nuclei for subsequent measurement of CD44, ICAM1, ICAM2, or Fibronectin intensity around or under the nuclei. The model achieved an Intersection over Union (IoU) score of 0.558 and an F1 Score of 0.793, reflecting its capability to distinguish tumor cell nuclei from HUVEC nuclei.</p> <h3>Specifications</h3> <ul> <li> <p>Model: StarDist for segmenting tumor cell nuclei from the DAPI fluorescence channel</p> </li> <li> <p>Training Dataset:</p> </li> <ul> <li> <p>Number of Images: 48 paired fluorescence microscopy images and label masks</p> </li> <li> <p>Microscope: Spinning disk confocal microscope (3i CSU-W1) with a 20x objective, NA 0.8</p> </li> <li> <p>Data Type: Fluorescence microscopy images of the DAPI channel with manually segmented masks</p> </li> <li> <p>File Format: TIFF (.tif)</p> </li> <ul> <li> <p>Fluorescence Images: 16-bit</p> </li> <li> <p>Masks: 8-bit</p> </li> </ul> <li> <p>Image Size: 920 x 920 pixels (Pixel size: 0.6337 x 0.6337 µm²)</p> </li> </ul> <li> <p>Model Capabilities:</p> </li> <ul> <li> <p>Segment Tumor Cell Nuclei: Detects individual tumor cell nuclei in the DAPI channel while distinguishing them from HUVEC nuclei</p> </li> <li> <p>Measure Intensity: Enables measurement of CD44, ICAM1, ICAM2, or Fibronectin intensity around or under tumor cell nuclei in respective channels</p> </li> </ul> <li> <p>Performance:</p> </li> <ul> <li> <p>Average IoU: 0.558</p> </li> <li> <p>Average F1 Score: 0.793</p> </li> </ul> <li> <p>Model Training: Conducted using ZeroCostDL4Mic (<a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki</a>)</p> </li> </ul> <div> <h3>Reference</h3> <div><strong>Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers</strong></div> </div> <div>Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet</div> <div>bioRxiv 2024.09.30.615654; doi: <a href="https://www.biorxiv.org/content/10.1101/2024.09.30.615654v1">https://doi.org/10.1101/2024.09.30.615654</a></div> <p> </p>
ZeroCostDL4Mic - Stardist 2D example training and test dataset (light)
<p><strong>Name</strong>: ZeroCostDL4Mic - Stardist 2D example training and test dataset (light)</p> <p>(see <a href="http://https//github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p><strong>Data type</strong>: Paired microscopy images (fluorescence) and corresponding masks</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy (SiR-DNA) and masks obtained via manual segmentation (see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist</a> for details about the segmentation)</p> <p><strong>Microscope</strong>: Spinning disk confocal microscope with a 20x 0.8 NA objective</p> <p><strong>Cell type</strong>: DCIS.COM LifeAct-RFP cells</p> <p><strong>File format</strong>: .tif (16-bit for fluorescence and 8 and 16-bit for the masks)</p> <p><strong>Image size</strong>: 1024x1024 (Pixel size: 634 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Johanna Jukkala<sup>1,2</sup> and Guillaume Jacquemet<sup>1,2</sup></p> <p><strong>Contact email</strong>: guillaume.jacquemet@abo.fi</p> <p><strong>Affiliation</strong> : </p> <p>1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland</p> <p>2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland</p> <p><strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.</p>
Combining StarDist and TrackMate example 2 - T cell dataset
<p><strong>Description</strong>: Contains a StarDist example training dataset, a test dataset, and the StarDist model generated using ZeroCostDL4Mic (see https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist)</p> <p><strong>Training dataset</strong>: 209 Paired microscopy images (brightfield) and corresponding masks</p> <p><strong>Microscopy data type</strong>: brightfield microscopy and masks obtained via manual segmentation (see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist</a> for details about the segmentation)</p> <p><strong>Microscope</strong>: Imaging was done using a 10x phase contrast objective at 37°C on a Zeiss Axiovert 200M microscope equipped with an automated X-Y stage and a Roper EMCCD camera. Time-lapse images were collected every 30 sec for 10 min using SlideBook 6 software (Intelligent Imaging Innovations).</p> <p><strong>File format</strong>: .tif (16-bit for brightfield images and 8 and 16-bit for the masks)</p> <p><strong>Image size</strong>: 1024x1024 (Pixel size: 645 nm)</p>
Combining StarDist and TrackMate example 1 - Breast cancer cell dataset
<p><strong>Description</strong>: Contains a StarDist example training dataset, a test dataset, and the StarDist model generated using ZeroCostDL4Mic (see https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist)</p> <p><strong>Training dataset</strong>: 72 Paired microscopy images (fluorescence) and corresponding masks</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy (SiR-DNA) and masks obtained via manual segmentation (see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist</a> for details about the segmentation)</p> <p><strong>Microscope</strong>: Spinning disk confocal microscope with a 20x 0.8 NA objective</p> <p><strong>Cell type</strong>: DCIS.COM Lifeact-RFP cells</p> <p><strong>File format</strong>: .tif (16-bit for fluorescence and 8 and 16-bit for the masks)</p> <p><strong>Image size</strong>: 1024x1024 (Pixel size: 634 nm)</p>
Combining StarDist and TrackMate - Extended data
<ul> <li><strong>Detailed step-by-step protocol explaining how to combine StarDist and TrackMate.</strong></li> </ul> <p> </p> <ul> <li><strong>Video 1: Automated tracking of breast cancer cell migrating collectively</strong></li> </ul> <p>MCF10DCIS.com cells, labeled with Sir-DNA, were recorded using a spinning disk confocal microscope and automatically tracked using StarDist and TrackMate. Local tracks are displayed.</p> <ul> <li><strong>Video 2: Automated tracking of T cell migrating on ICAM-1</strong></li> </ul> <p>Activated T cell plated ICAM-1 were recorded using a brightfield microscope and automatically tracked using StarDist and TrackMate. Local tracks are displayed.</p> <ul> <li><strong>Video 3: Automated tracking of cancer cells flowing in a microfluidic chamber</strong></li> </ul> <p>AsPC1 pancreatic cancer cells flowing in a microfluidic chamber were recorded live using a brightfield microscope and automatically tracked using StarDist and TrackMate. Local tracks are displayed.</p>
Zebrafish Brain Vasculature and EC Nuclei 3dpf Light-Sheet (For StarDist)
<p>Small dataset of the zebrafish brain vasculature and endothelial cell nuclei acquired at 3 days post fertilization (dpf) with a Zeiss Z.2 light-sheet microscope. Transgenic reporter lines used: blood vessels <em>Tg(kdrl:HRAS-mCherry)</em><sup><em>s916</em></sup> labels EC membrane (Chi et al., 2008) and EC nuclei <em>Tg(kdr:nls-eGFP)</em><sup><em>zf109</em></sup> labels EC nuclei (Blum et al., 2008).</p> <p>Data were acquired by Elisabeth Kugler in 2018 at the University of Sheffield conforming to UK Home Office regulations and were performed under Home Office Project Licence 70/8588 held by TJAC. - Data are part of the publication: Kugler et al., 2019 as well as Dr Kugler's thesis.</p> <p>Dataset consists only of 3 samples and is used as a sample dataset to train students on StarDist (https://github.com/stardist/; Schmidt, et al. 2018; Weigert, et al. 2020).</p> <p>Image Acquisition Details:<br> Datasets in Sheffield were obtained using a Zeiss Z.1 light sheet microscope with a water-dipping detection objective (Plan-Apochromat 20×/1.0 Corr nd = 1.38) and a scientific complementary metal-oxide semiconductor (sCMOS) detection unit. Data were acquired with activated pivot scan, dual-sided illumination and online fusion; properties of acquired data are as follows: 0.7× zoom, 16bit image depth, 1,920 × 1,920px image size and minimum z-stack interval (approx. 0.33 × 0.33× 0.5 μm). Green and red fluorophores were excited using 488 nm and 561 nm laser, respectively. Used filters in sequential tracks for multi-colour images were LP560 for both and BP505-545 and LP585, respectively. Samples were embedded in 1% or 2% LMP-agarose containing 200 mg/l Tricaine (MS-222, Sigma). The image acquisition chamber was filled with E3 plus Tricaine (200 mg/ml) and maintained at 28°C.</p>
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