Share Email Print
cover

Proceedings Paper

Automatic computational labeling of glomerular textural boundaries
Author(s): Brandon Ginley; John E. Tomaszewski; Pinaki Sarder
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

The glomerulus, a specialized bundle of capillaries, is the blood filtering unit of the kidney. Each human kidney contains about 1 million glomeruli. Structural damages in the glomerular micro-compartments give rise to several renal conditions; most severe of which is proteinuria, where excessive blood proteins flow freely to the urine. The sole way to confirm glomerular structural damage in renal pathology is by examining histopathological or immunofluorescence stained needle biopsies under a light microscope. However, this method is extremely tedious and time consuming, and requires manual scoring on the number and volume of structures. Computational quantification of equivalent features promises to greatly ease this manual burden. The largest obstacle to computational quantification of renal tissue is the ability to recognize complex glomerular textural boundaries automatically. Here we present a computational pipeline to accurately identify glomerular boundaries with high precision and accuracy. The computational pipeline employs an integrated approach composed of Gabor filtering, Gaussian blurring, statistical F-testing, and distance transform, and performs significantly better than standard Gabor based textural segmentation method. Our integrated approach provides mean accuracy/precision of 0.89/0.97 on n = 200Hematoxylin and Eosin (HE) glomerulus images, and mean 0.88/0.94 accuracy/precision on n = 200 Periodic Acid Schiff (PAS) glomerulus images. Respective accuracy/precision of the Gabor filter bank based method is 0.83/0.84 for HE and 0.78/0.8 for PAS. Our method will simplify computational partitioning of glomerular micro-compartments hidden within dense textural boundaries. Automatic quantification of glomeruli will streamline structural analysis in clinic, and can help realize real time diagnoses and interventions.

Paper Details

Date Published: 1 March 2017
PDF: 6 pages
Proc. SPIE 10140, Medical Imaging 2017: Digital Pathology, 101400G (1 March 2017); doi: 10.1117/12.2254517
Show Author Affiliations
Brandon Ginley, Univ. at Buffalo (United States)
John E. Tomaszewski, Univ. at Buffalo (United States)
Pinaki Sarder, Univ. at Buffalo (United States)


Published in SPIE Proceedings Vol. 10140:
Medical Imaging 2017: Digital Pathology
Metin N. Gurcan; John E. Tomaszewski, Editor(s)

© SPIE. Terms of Use
Back to Top