Deep Learning for Virtual Histological Staining of Bright-Field Microscopic Images of Unlabeled Carotid Artery Tissue.

ABSTRACT:

Purpose

Histological analysis of artery tissue samples is a widely used method for diagnosis and quantification of cardiovascular diseases. However, the variable and labor-intensive tissue staining procedures hinder efficient and informative histological image analysis.

Procedures

In this study, we developed a deep learning-based method to transfer bright-field microscopic images of unlabeled tissue sections into equivalent bright-field images of histologically stained versions of the same samples. We trained a convolutional neural network to build maps between the unstained images and histologically stained images using a conditional generative adversarial network model.

Results

The results of a blind evaluation by board-certified pathologists illustrate that the vir

SUBMITTER: Li D 

PROVIDER: S-EPMC7497459 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature