Light-sheet flow cytometry for label-free classification of acute and chronic myeloid leukemic cells with machine learning

Zhi Li; Guosheng Li; Jun Peng; Xuantao Su

doi:10.1117/12.2579964

5 November 2020 Light-sheet flow cytometry for label-free classification of acute and chronic myeloid leukemic cells with machine learning

Zhi Li, Guosheng Li, Jun Peng, Xuantao Su

Author Affiliations +

Proceedings Volume 11566, AOPC 2020: Optical Spectroscopy and Imaging; and Biomedical Optics; 1156614 (2020) https://doi.org/10.1117/12.2579964
Event: Applied Optics and Photonics China (AOPC 2020), 2020, Beijing, China

Abstract

Conventional flow cytometry has been used for leukemia characterization via fluorescence measurements. Here we measured the 2D light scattering patterns of label-free HL-60 cells (human acute myeloid leukemic cells) and K562 cells (human chronic myeloid leukemic cells) with a light-sheet illuminated flow cytometer. Approximately 70 light scattering patterns of leukemia cells were obtained in a one minute video taken by this cytometer operating at 50 frames per second. Local binary pattern (LBP) was used to extract features of the 2D light scattering images, which were then analyzed by the support vector machine (SVM) algorithm. An accuracy rate of 98.23% was obtained for the label-free classification of these two kinds of leukemia cells, with a specificity of 99.28% and a sensitivity of 97.22%. The combination of light-sheet flow cytometry with machine learning may be helpful for leukemia subtyping diagnosis in clinics.

Citation Download Citation

Zhi Li, Guosheng Li, Jun Peng, and Xuantao Su "Light-sheet flow cytometry for label-free classification of acute and chronic myeloid leukemic cells with machine learning", Proc. SPIE 11566, AOPC 2020: Optical Spectroscopy and Imaging; and Biomedical Optics, 1156614 (5 November 2020); https://doi.org/10.1117/12.2579964

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