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

TL;DR

This paper presents a rapid, accurate, and adaptable automated method using multi-radial LBP features and SVM for glomeruli detection in large histopathology images, with potential clinical applications.

Contribution

The study introduces a novel multi-radial LBP feature-based SVM approach for efficient glomeruli segmentation across species and staining methods, enhancing speed and accuracy.

Findings

Achieves >90% precision and >70% recall in glomeruli detection.

Requires approximately 15 seconds for training and 2 minutes for detection per WSI.

Combining LBP features with deep neural networks reduces false positives below 3%.

Abstract

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.