Segmentation of Glomeruli Within Trichrome Images Using Deep Learning.

ABSTRACT:

Introduction

The number of glomeruli and glomerulosclerosis evaluated on kidney biopsy slides constitute standard components of a renal pathology report. Prevailing methods for glomerular assessment remain manual, labor intensive, and nonstandardized. We developed a deep learning framework to accurately identify and segment glomeruli from digitized images of human kidney biopsies.

Methods

Trichrome-stained images (n = 275) from renal biopsies of 171 patients with chronic kidney disease treated at the Boston Medical Center from 2009 to 2012 were analyzed. A sliding window operation was defined to crop each original image to smaller images. Each cropped image was then evaluated by at least 3 experts into 3 categories: (i) no glomerulus, (ii) normal or partially sclerose

SUBMITTER: Kannan S 

PROVIDER: S-EPMC6612039 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature