StarDist_BF_Monocytes_dataset

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