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Virtual Staining - Nuclei

Updated: Dec 6, 2022



This model transforms 2D brightfield images to fluorescence. The model simulates DAPI fluorescent stains targeting nuclei.


The training data for this model includes pairs of images captured at 20x magnification on differential interference contrast (DIC) microscopy for input and fluorescence microscopy for ground truth. The model is trained by Pixel2Pixel [1] with RCAUNet (U-Net with residual channel attention blocks) [2] as the generator and least square generative adversarial network (LSGAN) [3] as the discriminator.



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Requirements

Make sure you have Python versions 3.5 to 3.7 installed with TensorFlow-GPU version 1.13.0 and above.