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We demonstrate a simple and effective automated method for the segmentationof glomeruli from large (~1 gigapixel) histopathological whole-slide images(WSIs) of thin renal tissue sections and biopsies, using an adaptation of thewell-known local binary patterns (LBP) image feature vector to train a supportvector machine (SVM) model. Our method offers high precision (>90%) andreasonable recall (>70%) for glomeruli from WSIs, is readily adaptable toglomeruli from multiple species, including mouse, rat, and human, and is robustto diverse slide staining methods. Using 5 Intel(R) Core(TM) i7-4790 CPUs with40 GB RAM, our method typically requires ~15 sec for training and ~2 min toextract glomeruli reproducibly from a WSI. Deploying a deep convolutionalneural network trained for glomerular recognition in tandem with the SVMsuffices to reduce false positives to below 3%. We also apply our LBP-baseddescriptor to successfully detect pathologic changes in a mouse model ofdiabetic nephropathy. We envision potential clinical and laboratoryapplications for this approach in the study and diagnosis of glomerulardisease, and as a means of greatly accelerating the construction of featuresets to fuel deep learning studies into tissue structure and pathology.

Multi-radial LBP Features as a Tool for Rapid Glomerular Detection and Assessment in Whole Slide Histopathology Images