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Protocols & Methods
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Multi-radial LBP Features as a Tool for Rapid Glomerular Detection and
Assessment in Whole Slide Histopathology Images.
Simon O, Yacoub R, Jain S, Tomaszewski JE, Sarder P
Pinaki Sarder on 12/3/2018
Volume : Pages
8 : 2032
We demonstrate a simple and effective automated method for the localization of
glomeruli in 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.
Investigators with authorship
University of Florida
Neuropathy & Neurocognition
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Please acknowledge all posters, manuscripts or scientific materials that were generated in part or whole using funds from the Diabetic Complications Consortium(DiaComp) using the following text:
Financial support for this work provided by the NIDDK Diabetic Complications Consortium (RRID:SCR_001415, www.diacomp.org), grants DK076169 and DK115255
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