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Proceedings Paper

Classification of platelet aggregates by intelligent imaging flow cytometry (Conference Presentation)

Paper Abstract

Platelets participate in both physiological hemostasis and pathological thrombosis by forming aggregates activated by various agonists. However, it has been considered impossible to identify the stimuli and classify the aggregates. Here we present an intelligent method for classifying platelet aggregates by agonist type based on the combination of high-throughput imaging flow cytometry and a convolutional neural network. It morphologically identifies the contributions of different agonists to platelet aggregation with high accuracy. The method is a powerful tool for studying the underlying mechanism of platelet aggregation and is expected to develop a new class of clinical diagnostics and therapeutics.

Paper Details

Date Published: 10 March 2020
Proc. SPIE 11250, High-Speed Biomedical Imaging and Spectroscopy V, 112500W (10 March 2020); doi: 10.1117/12.2544194
Show Author Affiliations
Yuqi Zhou, The Univ. of Tokyo (Japan)
Atsushi Yasumoto, The Univ. of Tokyo (Japan)
Cheng Lei, Wuhan Univ. (China)
Chun-Jung Huang, National Chiao Tung Univ. (Taiwan)
Hirofumi Kobayashi, Chan Zuckerberg Biohub (United States)
Yunzhao Wu, The Univ. of Tokyo (Japan)
Sheng Yan, The Univ. of Tokyo (Japan)
Chia-Wei Sun, National Chiao Tung Univ. (Taiwan)
Yutaka Yatomi, The Univ. of Tokyo (Japan)
Keisuke Goda, The Univ. of Tokyo (Japan)

Published in SPIE Proceedings Vol. 11250:
High-Speed Biomedical Imaging and Spectroscopy V
Kevin K. Tsia; Keisuke Goda, Editor(s)

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