Automatic Counting System of Red Blood Cells Based on Fourier Ptychographic Microscopy

Shushan Wang; Tingfa Xu; Jizhou Zhang; Xin Wang; Yiwen Chen; Jinhua Zhang

doi:10.1007/978-981-15-8411-4_119

Automatic Counting System of Red Blood Cells Based on Fourier Ptychographic Microscopy

Shushan Wang, Tingfa Xu^*, Jizhou Zhang, Xin Wang, Yiwen Chen, Jinhua Zhang

^*Corresponding author for this work

School of Optics and Photonics

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Red blood cell (RBC) counting is of great medical significance in clinical examination. Commonly, the cell counting task is completed by microscopic examination, which requires a high resolution. This paper proposes an automatic counting system of red blood cells based on Fourier ptychographic microscopy (FPM) and estimates the RBC number via a convolutional neural network (CNN). The counting network is based on a regression model, using a VGG-16 network combined with a feature pyramid network (FPN). The experimental results show that the mean absolute percentage error (MAPE) of our counting network can reach 0.86%, which means a high accuracy.

Original language	English
Title of host publication	Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems
Editors	Qilian Liang, Wei Wang, Xin Liu, Zhenyu Na, Xiaoxia Li, Baoju Zhang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	891-898
Number of pages	8
ISBN (Print)	9789811584107
DOIs	https://doi.org/10.1007/978-981-15-8411-4_119
Publication status	Published - 2021
Event	9th International Conference on Communications, Signal Processing, and Systems, CSPS 2020 - Changbaishan, China Duration: 4 Jul 2020 → 5 Jul 2020

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	654 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	9th International Conference on Communications, Signal Processing, and Systems, CSPS 2020
Country/Territory	China
City	Changbaishan
Period	4/07/20 → 5/07/20

Keywords

Convolutional neural networks
Feature pyramid network
Fourier ptychographic microscopy
Red blood cell counting

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10.1007/978-981-15-8411-4_119

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Wang, S., Xu, T., Zhang, J., Wang, X., Chen, Y., & Zhang, J. (2021). Automatic Counting System of Red Blood Cells Based on Fourier Ptychographic Microscopy. In Q. Liang, W. Wang, X. Liu, Z. Na, X. Li, & B. Zhang (Eds.), Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems (pp. 891-898). (Lecture Notes in Electrical Engineering; Vol. 654 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-15-8411-4_119

Wang, Shushan ; Xu, Tingfa ; Zhang, Jizhou et al. / Automatic Counting System of Red Blood Cells Based on Fourier Ptychographic Microscopy. Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems. editor / Qilian Liang ; Wei Wang ; Xin Liu ; Zhenyu Na ; Xiaoxia Li ; Baoju Zhang. Springer Science and Business Media Deutschland GmbH, 2021. pp. 891-898 (Lecture Notes in Electrical Engineering).

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title = "Automatic Counting System of Red Blood Cells Based on Fourier Ptychographic Microscopy",

abstract = "Red blood cell (RBC) counting is of great medical significance in clinical examination. Commonly, the cell counting task is completed by microscopic examination, which requires a high resolution. This paper proposes an automatic counting system of red blood cells based on Fourier ptychographic microscopy (FPM) and estimates the RBC number via a convolutional neural network (CNN). The counting network is based on a regression model, using a VGG-16 network combined with a feature pyramid network (FPN). The experimental results show that the mean absolute percentage error (MAPE) of our counting network can reach 0.86%, which means a high accuracy.",

keywords = "Convolutional neural networks, Feature pyramid network, Fourier ptychographic microscopy, Red blood cell counting",

author = "Shushan Wang and Tingfa Xu and Jizhou Zhang and Xin Wang and Yiwen Chen and Jinhua Zhang",

note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 9th International Conference on Communications, Signal Processing, and Systems, CSPS 2020 ; Conference date: 04-07-2020 Through 05-07-2020",

year = "2021",

doi = "10.1007/978-981-15-8411-4_119",

language = "English",

isbn = "9789811584107",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "891--898",

editor = "Qilian Liang and Wei Wang and Xin Liu and Zhenyu Na and Xiaoxia Li and Baoju Zhang",

booktitle = "Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems",

address = "Germany",

}

Wang, S, Xu, T, Zhang, J, Wang, X, Chen, Y & Zhang, J 2021, Automatic Counting System of Red Blood Cells Based on Fourier Ptychographic Microscopy. in Q Liang, W Wang, X Liu, Z Na, X Li & B Zhang (eds), Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering, vol. 654 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 891-898, 9th International Conference on Communications, Signal Processing, and Systems, CSPS 2020, Changbaishan, China, 4/07/20. https://doi.org/10.1007/978-981-15-8411-4_119

Automatic Counting System of Red Blood Cells Based on Fourier Ptychographic Microscopy. / Wang, Shushan; Xu, Tingfa; Zhang, Jizhou et al.
Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems. ed. / Qilian Liang; Wei Wang; Xin Liu; Zhenyu Na; Xiaoxia Li; Baoju Zhang. Springer Science and Business Media Deutschland GmbH, 2021. p. 891-898 (Lecture Notes in Electrical Engineering; Vol. 654 LNEE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Automatic Counting System of Red Blood Cells Based on Fourier Ptychographic Microscopy

AU - Wang, Shushan

AU - Xu, Tingfa

AU - Zhang, Jizhou

AU - Wang, Xin

AU - Chen, Yiwen

AU - Zhang, Jinhua

PY - 2021

Y1 - 2021

N2 - Red blood cell (RBC) counting is of great medical significance in clinical examination. Commonly, the cell counting task is completed by microscopic examination, which requires a high resolution. This paper proposes an automatic counting system of red blood cells based on Fourier ptychographic microscopy (FPM) and estimates the RBC number via a convolutional neural network (CNN). The counting network is based on a regression model, using a VGG-16 network combined with a feature pyramid network (FPN). The experimental results show that the mean absolute percentage error (MAPE) of our counting network can reach 0.86%, which means a high accuracy.

AB - Red blood cell (RBC) counting is of great medical significance in clinical examination. Commonly, the cell counting task is completed by microscopic examination, which requires a high resolution. This paper proposes an automatic counting system of red blood cells based on Fourier ptychographic microscopy (FPM) and estimates the RBC number via a convolutional neural network (CNN). The counting network is based on a regression model, using a VGG-16 network combined with a feature pyramid network (FPN). The experimental results show that the mean absolute percentage error (MAPE) of our counting network can reach 0.86%, which means a high accuracy.

KW - Convolutional neural networks

KW - Feature pyramid network

KW - Fourier ptychographic microscopy

KW - Red blood cell counting

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U2 - 10.1007/978-981-15-8411-4_119

DO - 10.1007/978-981-15-8411-4_119

M3 - Conference contribution

AN - SCOPUS:85111397695

SN - 9789811584107

T3 - Lecture Notes in Electrical Engineering

SP - 891

EP - 898

BT - Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems

A2 - Liang, Qilian

A2 - Wang, Wei

A2 - Liu, Xin

A2 - Na, Zhenyu

A2 - Li, Xiaoxia

A2 - Zhang, Baoju

PB - Springer Science and Business Media Deutschland GmbH

T2 - 9th International Conference on Communications, Signal Processing, and Systems, CSPS 2020

Y2 - 4 July 2020 through 5 July 2020

ER -

Wang S, Xu T, Zhang J, Wang X, Chen Y, Zhang J. Automatic Counting System of Red Blood Cells Based on Fourier Ptychographic Microscopy. In Liang Q, Wang W, Liu X, Na Z, Li X, Zhang B, editors, Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems. Springer Science and Business Media Deutschland GmbH. 2021. p. 891-898. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-15-8411-4_119

Automatic Counting System of Red Blood Cells Based on Fourier Ptychographic Microscopy

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