This repository includes a StarDist deep learning model and its training and validation datasets for detecting mononucleated cells perfused over an endothelial cell monolayer. The model was trained on 27 manually annotated images and achieved an average F1 Score of 0.941. The dataset and model are helpful for biomedical research, especially in studying interactions between mononucleated and endothelial cells. Specifications Model: StarDist for mononucleated cell detection on endothelial cells Training Dataset: Number of Images: 27 paired brightfield microscopy images and label masks Microscope: Nikon Eclipse Ti2-E, 20x objective Data Type: Brightfield microscopy images with manually segmented masks File Format: TIFF (.tif) Brightfield Images: 16-bit Masks: 8-bit Image Size: 1024 x 1022 pixels (Pixel size: 650 nm) Training Parameters: Epochs: 400 Patch Size: 992 x 992 pixels Batch Size: 2 Performance: Average F1 Score: 0.941 Average IoU: 0.831 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

2024