Development and evaluation of deep learning-based segmentation of histologic structures in the kidney cortex with multiple histologic stains.

ABSTRACT: The application of deep learning for automated segmentation (delineation of boundaries) of histologic primitives (structures) from whole slide images can facilitate the establishment of novel protocols for kidney biopsy assessment. Here, we developed and validated deep learning networks for the segmentation of histologic structures on kidney biopsies and nephrectomies. For development, we examined 125 biopsies for Minimal Change Disease collected across 29 NEPTUNE enrolling centers along with 459 whole slide images stained with Hematoxylin & Eosin (125), Periodic Acid Schiff (125), Silver (102), and Trichrome (107) divided into training, validation and testing sets (ratio 6:1:3). Histologic structures were manually segmented (30048 total annotations) by five nephropathologists. Twenty deep

SUBMITTER: Jayapandian CP 

PROVIDER: S-EPMC8414393 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature