pix2pix_HUVEC_nuclei_cancer_cells_dataset

This repository contains a Pix2Pix deep learning model to generate synthetic nuclear staining from brightfield images. The model was trained on 258 paired brightfield and fluorescent microscopy images of circulating cancer cells perfused over an endothelial cell monolayer. To improve performance, the dataset was augmented computationally by a factor of 8. 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 its performance metrics and visual fidelity when compared to ground truth images, achieving an average SSIM score of 0.755 and an LPIPS score of 0.120. Specifications Model: Pix2Pix for generating synthetic nuclear staining from brightfield images Training Dataset: Cancer Cells: 258 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: Circulating Cancer Cells: SSIM Score: 0.755 LPIPS Score: 0.120 Model Selection: Models were selected 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 Biorxiv paper