Multi-modal Image Synthesis for Virtual Tissue Staining in Pathology

This invention is a novel multi-modal image synthesis approach for virtual tissue staining. Tissue samples are prepared for sectioning using formalin-fixed/paraffin-embedded or frozen methods, and unstained or stained with fast, cheap chemical stains. The stained sections are then imaged using standard digital light microscopes. This invention reduces the risk of image blurring by estimating what the output section would look like if stained using the input stain. Multiple estimated input images are merged using pixel-wise max procedure, and the most important information from each image is retrieved. The latent representations of each image are extracted and combined to form a synthesized image that contains full information from each image type. The network is then trained with ≥100 image sets of similar tissues, each set comprising the synthesized images along with the output image from a tissue sample. Trained networks are prospectively evaluated using ≥10 additional image sets acquired and processed as for training.

#TissueStainingMethods #MultimodalImageSynthesis #MultimodalImageGeneration #VirtualTissueStaining #DiseaseDiagnosis #Diagnosis #PathologyLaboratory

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