High-throughput label-free cell detection and counting from diffraction patterns with deep fully convolutional neural networks.

ABSTRACT: Digital holographic microscopy (DHM) is a promising technique for the study of semitransparent biological specimen such as red blood cells (RBCs). It is important and meaningful to detect and count biological cells at the single cell level in biomedical images for biomarker discovery and disease diagnostics. However, the biological cell analysis based on phase information of images is inefficient due to the complexity of numerical phase reconstruction algorithm applied to raw hologram images. New cell study methods based on diffraction pattern directly are desirable. Deep fully convolutional networks (FCNs) were developed on raw hologram images directly for high-throughput label-free cell detection and counting to assist the biological cell analysis in the future. The raw diffraction patte

SUBMITTER: Yi F 

PROVIDER: S-EPMC7939515 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature