This repository includes a StarDist deep learning model and its training dataset designed for segmenting cancer cells perfused over an endothelial cell monolayer captured at 10x magnification. The model was trained on 77 manually annotated images, with the dataset being computationally augmented during training by a factor of 8. The model was trained for 500 epochs and achieved an average F1 Score of 0.968, indicating high accuracy in segmenting cancer cells on endothelial cells. Specifications Model: StarDist for cancer cell segmentation on endothelial cells (10x magnification) Training Dataset: Number of Images: 77 paired brightfield microscopy images and label masks Augmented Dataset: Computational augmentation by a factor of 8 during training Microscope: Nikon Eclipse Ti2-E, 10x objective Data Type: Brightfield microscopy images with manually segmented masks File Format: TIFF (.tif) Brightfield Images: 16-bit Masks: 8-bit or 16-bit Image Size: 1024 x 1022 pixels (pixel size: 1.3148 μm) Training Parameters: Epochs: 500 Patch Size: 992 x 992 pixels Batch Size: 2 Performance: Average F1 Score: 0.968 Average IoU: 0.882 Model Training: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki) Reference Biorxiv paper

2024