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 &micro;m&sup2;)</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&nbsp;Follain,&nbsp;Sujan&nbsp;Ghimire,&nbsp;Joanna W.&nbsp;Pylv&auml;n&auml;inen,&nbsp;Monika&nbsp;Vaitkevičiūtė,&nbsp;Diana&nbsp;Wurzinger,&nbsp;Camilo&nbsp;Guzm&aacute;n,&nbsp;James RW&nbsp;Conway,&nbsp;Michal&nbsp;Dibus,&nbsp;Sanna&nbsp;Oikari,&nbsp;Kirsi&nbsp;Rilla,&nbsp;Marko&nbsp;Salmi,&nbsp;Johanna&nbsp;Ivaska,&nbsp;Guillaume&nbsp;Jacquemet</div> <div>bioRxiv&nbsp;2024.09.30.615654;&nbsp;doi:&nbsp;<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>&nbsp;</p>