We introduce the concept of a virtual biopsy, by using generative neural networks to synthesize virtual H&E sections from OCT images. To do this, we have developed a novel technique, “optical barcoding”, which has allowed us to construct a large dataset of OCT-H&E image pairs of human skin collected during Mohs surgery. Our results demonstrate the ability to use OCT images to generate high-fidelity virtual H&E sections and entire 3D H&E volumes. We hope that our novel method will allow clinicians to circumvent the time- and resource-consuming step of an invasive biopsy procedure.
We introduce a novel technique, “optical barcoding”, which enables us to repeatedly extract the 2D OCT slice from a 3D OCT volume that corresponds to a given H and E tissue section, with high alignment precision of 25 microns. Our method is based on marking a specific geometric pattern that is preserved through the standard histological process and encodes all orientation, position and scaling information about how the section was cut. We demonstrate the robustness of our novel technique by collecting hundreds of high-quality OCT-H and E image pairs from more than 30 different human skin samples that were collected during Mohs surgery.
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