Spectral image microscopy for label-free blood and cancer cell identification

Mark Gesley; Robert Goldsby; Stephen Lane; Romin Puri

doi:10.1117/12.2507474

4 March 2019 Spectral image microscopy for label-free blood and cancer cell identification

Mark Gesley, Robert Goldsby, Stephen Lane, Romin Puri

Proceedings Volume 10890, Label-free Biomedical Imaging and Sensing (LBIS) 2019; 108900H (2019) https://doi.org/10.1117/12.2507474
Event: SPIE BiOS, 2019, San Francisco, California, United States

Abstract

New forms of cancer cell identification coupled with faster detection and better accuracy may enhance diagnostic capabilities. The purpose of this study is to improve recognition of minimal residual disease from peripheral blood samples. Spectral images are generated by optical microscopy using filtered broadband visible light elastically scattered from human blood and cancer cells. Exogenous tags, like CD markers may introduce a label bias and are not required. A training cell may be validated without detailed knowledge of intra-cellular spectra used to classify random cells. Spectral object classification is scalable to any number of cell types. Small samples of erythrocytes, leukocytes, Jurkat cancer and non-small lung cell adenocarcinoma are accurately classified and associated with unique spatial-spectral characteristics.

Conference Presentation

Citation Download Citation

Mark Gesley, Robert Goldsby, Stephen Lane, and Romin Puri "Spectral image microscopy for label-free blood and cancer cell identification", Proc. SPIE 10890, Label-free Biomedical Imaging and Sensing (LBIS) 2019, 108900H (4 March 2019); https://doi.org/10.1117/12.2507474

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available