Region-Based Convolutional Neural Nets for Localization of Glomeruli in Trichrome-Stained Whole Kidney Sections.

ABSTRACT: Background Histologic examination of fixed renal tissue is widely used to assess morphology and the progression of disease. Commonly reported metrics include glomerular number and injury. However, characterization of renal histology is a time-consuming and user-dependent process. To accelerate and improve the process, we have developed a glomerular localization pipeline for trichrome-stained kidney sections using a machine learning image classification algorithm.Methods We prepared 4-μm slices of kidneys from rats of various genetic backgrounds that were subjected to different experimental protocols and mounted the slices on glass slides. All sections used in this analysis were trichrome stained and imaged in bright field at a minimum resolution of 0.92 μm per p

SUBMITTER: Bukowy JD 

PROVIDER: S-EPMC6065078 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature