Virtual Staining, Segmentation, and Classification of Blood Smears for Label-Free Hematology Analysis.

ABSTRACT: Objective and Impact Statement. We present a fully automated hematological analysis framework based on single-channel (single-wavelength), label-free deep-ultraviolet (UV) microscopy that serves as a fast, cost-effective alternative to conventional hematology analyzers. Introduction. Hematological analysis is essential for the diagnosis and monitoring of several diseases but requires complex systems operated by trained personnel, costly chemical reagents, and lengthy protocols. Label-free techniques eliminate the need for staining or additional preprocessing and can lead to faster analysis and a simpler workflow. In this work, we leverage the unique capabilities of deep-UV microscopy as a label-free, molecular imaging technique to develop a deep learning-based pipeline that e

SUBMITTER: Kaza N 

PROVIDER: S-EPMC10521747 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature