TY - JOUR
T1 - Multi-radial LBP Features as a Tool for Rapid Glomerular Detection and Assessment in Whole Slide Histopathology Images
AU - Simon, Olivier
AU - Yacoub, Rabi
AU - Jain, Sanjay
AU - Tomaszewski, John E.
AU - Sarder, Pinaki
N1 - Funding Information:
We thank the histology core facility at Pathology & Anatomical Sciences department, University at Buffalo, for performing histopathological staining of tissues. We thank Dr. Tracey Ignatowski (Pathology and Anatomical Sciences, University at Buffalo) for providing us the rat renal tissues for this work. The project was supported partially by the faculty startup fund from the Pathology and Anatomical Sciences Department, University at Buffalo, partially by the UB Impact Award, and partially by the DiaComp Pilot and Feasibility Program grant #32307-5. We thank NVIDIA Corporation for the donation of the Titan X Pascal GPU used for this research (NVIDIA, Santa Clara, CA).
Publisher Copyright:
© 2018 The Author(s).
PY - 2018/12/1
Y1 - 2018/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85041610167&partnerID=8YFLogxK
U2 - 10.1038/s41598-018-20453-7
DO - 10.1038/s41598-018-20453-7
M3 - Article
C2 - 29391542
AN - SCOPUS:85041610167
SN - 2045-2322
VL - 8
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 2032
ER -