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Proceedings Paper

Intelligent frequency-shifted optofluidic time-stretch quantitative phase imaging (Conference Presentation)

Paper Abstract

The traditional diagnosis of leukemia relies on pathologists to observe and classify cells on bone marrow smears, which is low-throughput, time-consuming, and subject to human bias. To overcome these limitations, we demonstrate intelligent frequency-shifted optofluidic time-stretch quantitative phase imaging (OTS-QPI) that acquires bright-field and quantitative phase images of white blood cells (WBCs) containing leukemia cells with high throughput (15,000 cells/s) for deep-learning-based classification. After trained with 64,000 images, a convolutional neural network (CNN) distinguishes three different types of leukemia cells from WBCs with an accuracy of over 96%. Our method provides new possibilities for high-throughput, label-free, and intelligent leukemia diagnosis.

Paper Details

Date Published: 11 March 2020
Proc. SPIE 11249, Quantitative Phase Imaging VI, 112490X (11 March 2020); doi: 10.1117/12.2544119
Show Author Affiliations
Yunzhao Wu, The Univ. of Tokyo (Japan)
Yuqi Zhou, The Univ. of Tokyo (Japan)
Chun-Jung Huang, National Chiao Tung Univ. (Taiwan)
Hirofumi Kobayashi, Chan Zuckerberg Biohub (United States)
Sheng Yan, The Univ. of Tokyo (Japan)
Yasuyuki Ozeki, The Univ. of Tokyo (Japan)
Chia-Wei M. Sun, National Chiao Tung Univ. (Taiwan)
Cheng Lei, Wuhan Univ. (China)
Keisuke Goda, The Univ. of Tokyo (Japan)

Published in SPIE Proceedings Vol. 11249:
Quantitative Phase Imaging VI
Yang Liu; Gabriel Popescu; YongKeun Park, Editor(s)

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