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