Label-Free White Blood Cell Classification Using Refractive Index Tomography and Deep Learning.

ABSTRACT: Objective and Impact Statement. We propose a rapid and accurate blood cell identification method exploiting deep learning and label-free refractive index (RI) tomography. Our computational approach that fully utilizes tomographic information of bone marrow (BM) white blood cell (WBC) enables us to not only classify the blood cells with deep learning but also quantitatively study their morphological and biochemical properties for hematology research. Introduction. Conventional methods for examining blood cells, such as blood smear analysis by medical professionals and fluorescence-activated cell sorting, require significant time, costs, and domain knowledge that could affect test results. While label-free imaging techniques that use a specimen's intrinsic contrast (e.g., multi

SUBMITTER: Ryu D 

PROVIDER: S-EPMC10521749 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature