This repository contains a Pix2Pix deep learning model to generate synthetic PECAM-1 staining from brightfield images. The model was trained on an initial dataset of 484 paired brightfield and fluorescent microscopy images, which was computationally augmented by a factor of 6 to enhance model performance. The training was conducted over 245 epochs with a patch size of 512x512, a batch size of 1, and a vanilla GAN loss function. The final model was selected based on quality metric scores and visual comparison to ground truth images, achieving an average SSIM score of 0.273 and an LPIPS score of 0.360 on the test dataset. Specifications Model: Pix2Pix for generating synthetic PECAM staining from brightfield images Training Dataset: Original Dataset: 484 paired brightfield and fluorescent microscopy images Augmented Dataset: Expanded to 2,904 paired images through computational augmentation Microscope: Nikon Eclipse Ti2-E, brightfield/fluorescence microscope with a 20x objective Data Type: Brightfield and fluorescent microscopy images File Format: TIFF (.tif), 16-bit Image Size: 1024 x 1022 pixels (Pixel size: 650 nm) Training Parameters: Epochs: 245 Patch Size: 512 x 512 pixels Batch Size: 1 Loss Function: Vanilla GAN loss function Model Performance: SSIM Score: 0.273 LPIPS Score: 0.360 Model Selection: The best model was chosen based on quality metric scores and visual inspection compared to ground truth images. 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