Detection of glomeruli in renal pathology by mutual comparison of multiple staining modalities

We evaluate the detection of glomerular structures in whole slide images (WSIs) of histopathological slides stained with multiple histochemical and immuno-histochemical staining using a convolutional neural network (CNN) based approach. We mutually compare the CNN performance on different stainings (Jones H&E, PAS, Sirius Red and CDIO) and we present a novel approach to improve glomeruli detection on one staining by taking into account the classification results from differently stained consecutive sections of the same tissue. Using this integrative approach, the detection rate (Fl-score) on a single stain can be improved by up to 30%.