Deep Learning-Based Segmentation and Quantification in Experimental Kidney Histopathology.

ABSTRACT:

Background

Nephropathologic analyses provide important outcomes-related data in experiments with the animal models that are essential for understanding kidney disease pathophysiology. Precision medicine increases the demand for quantitative, unbiased, reproducible, and efficient histopathologic analyses, which will require novel high-throughput tools. A deep learning technique, the convolutional neural network, is increasingly applied in pathology because of its high performance in tasks like histology segmentation.

Methods

We investigated use of a convolutional neural network architecture for accurate segmentation of periodic acid-Schiff-stained kidney tissue from healthy mice and five murine disease models and from other species used in preclinical research. We trained the

SUBMITTER: Bouteldja N 

PROVIDER: S-EPMC7894663 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature