This repository contains a Pix2Pix deep learning model designed to generate synthetic nuclear staining from brightfield images of circulating immune cells. The model was trained on a dataset of 226 paired brightfield and fluorescent microscopy images, which were augmented computationally by a factor of 8 to enhance model performance. The model was trained over 400 epochs using 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 0.756 and an LPIPS score of 0.130. Specifications Model: Pix2Pix for generating synthetic nuclear staining from brightfield images of circulating immune cells Training Dataset: Immune Cells: 226 paired brightfield and fluorescent microscopy images 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: 400 Patch Size: 512 x 512 pixels Batch Size: 1 Loss Function: Vanilla GAN loss function Model Performance: Immune Cells: SSIM Score: 0.756 LPIPS Score: 0.130 Model Selection: Models were 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