Re-staining pathology images by FCNN

In histopathology, pathologic tissue samples are stained using one of various techniques according to the desired features to be observed in microscopic examination. One problem is that staining is irreversible. Once a tissue slice is stained using a technique, it cannot be re-stained using another. In this work, we propose a method for simulated re-staining using a Fully Convolutional Neural Network (FCNN). We convert a digitally scanned pathology image of a sample, stained using one technique, into another image with a different simulated stain. The challenge is that the ground truth cannot be obtained: The network needs training data, which in this case would be pairs of images of a sample stained in two different techniques. We overcome this problem by using the images of consecutive slices that are stained using the two distinct techniques, screening for morphological similarity by comparing their density components in the HSD color space. We demonstrate the effectiveness of the method in the case of converting hematoxylin and eosin-stained images into Masson's trichrome-stained images.