Sequence-level Supervised Deep Neural Networks for Mitosis Event Detection in Time-Lapse Microscopy Images

Siteng Chen; Ao Li; Janet Roveda

doi:10.1109/BIBM49941.2020.9313500

Sequence-level Supervised Deep Neural Networks for Mitosis Event Detection in Time-Lapse Microscopy Images

Siteng Chen, Ao Li, Janet Roveda

Electrical and Computer Engineering

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

Abstract

Automatic mitosis detection is a key step in measuring cell proliferation and analyzing the responses to various stimuli. Current deep neural networks can learn complex visual features and capture long-range temporal dependencies. However, the state-of-the-art mitosis detection models require massive ground truth annotations which is labor intensive in biomedical experiments. Therefore, we propose a sequence-level supervised neural networks model to detect mitosis events at pixel-and-frame level. By using binary labels, the proposed network is trained to predict the presence of mitosis for the input microscopy sequences. Then we leverage the feature map produced by the proposed network to localize the cell division. The proposed model achieved a detection F1-score 0.881.With significantly less amount of ground truth in the training data, our method achieved competitive performance compared with the state-of-art fully supervised mitosis detection methods.

Original language	English (US)
Title of host publication	Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
Editors	Taesung Park, Young-Rae Cho, Xiaohua Tony Hu, Illhoi Yoo, Hyun Goo Woo, Jianxin Wang, Julio Facelli, Seungyoon Nam, Mingon Kang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2570-2571
Number of pages	2
ISBN (Electronic)	9781728162157
DOIs	https://doi.org/10.1109/BIBM49941.2020.9313500
State	Published - Dec 16 2020
Event	2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 - Virtual, Seoul, Korea, Republic of Duration: Dec 16 2020 → Dec 19 2020

Publication series

Name	Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

Conference

Conference	2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
Country/Territory	Korea, Republic of
City	Virtual, Seoul
Period	12/16/20 → 12/19/20

Keywords

convolutional long-short-term memory
deep learning
microscopy imaging
mitosis detection
weakly-supervise

ASJC Scopus subject areas

Computer Science Applications
Information Systems and Management
Medicine (miscellaneous)
Health Informatics

Access to Document

10.1109/BIBM49941.2020.9313500

Cite this

Chen, S., Li, A., & Roveda, J. (2020). Sequence-level Supervised Deep Neural Networks for Mitosis Event Detection in Time-Lapse Microscopy Images. In T. Park, Y.-R. Cho, X. T. Hu, I. Yoo, H. G. Woo, J. Wang, J. Facelli, S. Nam, & M. Kang (Eds.), Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 (pp. 2570-2571). Article 9313500 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM49941.2020.9313500

Sequence-level Supervised Deep Neural Networks for Mitosis Event Detection in Time-Lapse Microscopy Images. / Chen, Siteng; Li, Ao ; Roveda, Janet.
Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. ed. / Taesung Park; Young-Rae Cho; Xiaohua Tony Hu; Illhoi Yoo; Hyun Goo Woo; Jianxin Wang; Julio Facelli; Seungyoon Nam; Mingon Kang. Institute of Electrical and Electronics Engineers Inc., 2020. p. 2570-2571 9313500 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020).

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

Chen, S, Li, A & Roveda, J 2020, Sequence-level Supervised Deep Neural Networks for Mitosis Event Detection in Time-Lapse Microscopy Images. in T Park, Y-R Cho, XT Hu, I Yoo, HG Woo, J Wang, J Facelli, S Nam & M Kang (eds), Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020., 9313500, Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, Institute of Electrical and Electronics Engineers Inc., pp. 2570-2571, 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, Virtual, Seoul, Korea, Republic of, 12/16/20. https://doi.org/10.1109/BIBM49941.2020.9313500

Chen S, Li A , Roveda J. Sequence-level Supervised Deep Neural Networks for Mitosis Event Detection in Time-Lapse Microscopy Images. In Park T, Cho YR, Hu XT, Yoo I, Woo HG, Wang J, Facelli J, Nam S, Kang M, editors, Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 2570-2571. 9313500. (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020). doi: 10.1109/BIBM49941.2020.9313500

Chen, Siteng ; Li, Ao ; Roveda, Janet. / Sequence-level Supervised Deep Neural Networks for Mitosis Event Detection in Time-Lapse Microscopy Images. Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. editor / Taesung Park ; Young-Rae Cho ; Xiaohua Tony Hu ; Illhoi Yoo ; Hyun Goo Woo ; Jianxin Wang ; Julio Facelli ; Seungyoon Nam ; Mingon Kang. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 2570-2571 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020).

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abstract = "Automatic mitosis detection is a key step in measuring cell proliferation and analyzing the responses to various stimuli. Current deep neural networks can learn complex visual features and capture long-range temporal dependencies. However, the state-of-the-art mitosis detection models require massive ground truth annotations which is labor intensive in biomedical experiments. Therefore, we propose a sequence-level supervised neural networks model to detect mitosis events at pixel-and-frame level. By using binary labels, the proposed network is trained to predict the presence of mitosis for the input microscopy sequences. Then we leverage the feature map produced by the proposed network to localize the cell division. The proposed model achieved a detection F1-score 0.881.With significantly less amount of ground truth in the training data, our method achieved competitive performance compared with the state-of-art fully supervised mitosis detection methods.",

keywords = "convolutional long-short-term memory, deep learning, microscopy imaging, mitosis detection, weakly-supervise",

author = "Siteng Chen and Ao Li and Janet Roveda",

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doi = "10.1109/BIBM49941.2020.9313500",

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AU - Roveda, Janet

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N2 - Automatic mitosis detection is a key step in measuring cell proliferation and analyzing the responses to various stimuli. Current deep neural networks can learn complex visual features and capture long-range temporal dependencies. However, the state-of-the-art mitosis detection models require massive ground truth annotations which is labor intensive in biomedical experiments. Therefore, we propose a sequence-level supervised neural networks model to detect mitosis events at pixel-and-frame level. By using binary labels, the proposed network is trained to predict the presence of mitosis for the input microscopy sequences. Then we leverage the feature map produced by the proposed network to localize the cell division. The proposed model achieved a detection F1-score 0.881.With significantly less amount of ground truth in the training data, our method achieved competitive performance compared with the state-of-art fully supervised mitosis detection methods.

AB - Automatic mitosis detection is a key step in measuring cell proliferation and analyzing the responses to various stimuli. Current deep neural networks can learn complex visual features and capture long-range temporal dependencies. However, the state-of-the-art mitosis detection models require massive ground truth annotations which is labor intensive in biomedical experiments. Therefore, we propose a sequence-level supervised neural networks model to detect mitosis events at pixel-and-frame level. By using binary labels, the proposed network is trained to predict the presence of mitosis for the input microscopy sequences. Then we leverage the feature map produced by the proposed network to localize the cell division. The proposed model achieved a detection F1-score 0.881.With significantly less amount of ground truth in the training data, our method achieved competitive performance compared with the state-of-art fully supervised mitosis detection methods.

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Sequence-level Supervised Deep Neural Networks for Mitosis Event Detection in Time-Lapse Microscopy Images

Abstract

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Keywords

ASJC Scopus subject areas

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