StarDist_Fluorescent_cells

This repository includes a StarDist deep learning model and its training and validation datasets for detecting fluorescently labeled cancer cells perfused over an endothelial cell monolayer. The model was trained on 66 images labeled with CellTrace and demonstrated high accuracy, achieving an average F1 Score of 0.877. The dataset and the trained model can be used for biomedical image analysis, particularly in cancer research.

Specifications





Model: StarDist for cancer cell detection




Training Dataset:






Number of Images: 66 paired fluorescent microscopy images and label masks




Microscope: Nikon Eclipse Ti2-E, 10x objective




Data Type: Fluorescent microscopy images with manually segmented masks




File Format: TIFF (.tif)






Brightfield Images: 16-bit




Masks: 8-bit





Image Size: 1024 x 1024 pixels (Pixel size: 1.3205 μm)





Training Parameters:






Epochs: 200




Patch Size: 1024 x 1024 pixels




Batch Size: 2





Performance:






Average F1 Score: 0.877




Average IoU: 0.646





Model Training: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki)





Reference

Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers


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

bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654