Automated vesicle fusion detection using Convolutional Neural Networks

Haohan Li; Zhaozheng Yin; Yingke Xu

doi:10.1109/ISBI.2017.7950497

Automated vesicle fusion detection using Convolutional Neural Networks

Haohan Li
, Zhaozheng Yin
, Yingke Xu

Biomedical Informatics

Missouri University of Science and Technology
Zhejiang University

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

Abstract

Quantitative analysis of vesicle-plasma membrane fusion events in the fluorescence microscopy, has been proven to be important in the vesicle exocytosis study. In this paper, we present a framework to automatically detect fusion events. First, an iterative searching algorithm is developed to extract image patch sequences containing potential events. Then, we propose an event image to integrate the critical image patches of a candidate event into a single-image joint representation as the input to Convolutional Neural Networks (CNNs). According to the duration of candidate events, we design three CNN architectures to automatically learn features for the fusion event classification. Compared on 9 challenging datasets, our proposed method showed very competitive performance and outperformed two state-of-the-arts.

Original language	English
Title of host publication	2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
Publisher	IEEE Computer Society
Pages	183-187
Number of pages	5
ISBN (Electronic)	9781509011711
DOIs	https://doi.org/10.1109/ISBI.2017.7950497
State	Published - Jun 15 2017
Event	14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 - Melbourne, Australia Duration: Apr 18 2017 → Apr 21 2017

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	14th IEEE International Symposium on Biomedical Imaging, ISBI 2017
Country/Territory	Australia
City	Melbourne
Period	04/18/17 → 04/21/17

Keywords

Convolutional neural networks
Fusion event identification
Vesicle exocytosis

Access to Document

10.1109/ISBI.2017.7950497

Cite this

@inproceedings{d0d94a466ea140ed8de8fc15146fda48,

title = "Automated vesicle fusion detection using Convolutional Neural Networks",

abstract = "Quantitative analysis of vesicle-plasma membrane fusion events in the fluorescence microscopy, has been proven to be important in the vesicle exocytosis study. In this paper, we present a framework to automatically detect fusion events. First, an iterative searching algorithm is developed to extract image patch sequences containing potential events. Then, we propose an event image to integrate the critical image patches of a candidate event into a single-image joint representation as the input to Convolutional Neural Networks (CNNs). According to the duration of candidate events, we design three CNN architectures to automatically learn features for the fusion event classification. Compared on 9 challenging datasets, our proposed method showed very competitive performance and outperformed two state-of-the-arts.",

keywords = "Convolutional neural networks, Fusion event identification, Vesicle exocytosis",

author = "Haohan Li and Zhaozheng Yin and Yingke Xu",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 ; Conference date: 18-04-2017 Through 21-04-2017",

year = "2017",

month = jun,

day = "15",

doi = "10.1109/ISBI.2017.7950497",

language = "English",

series = "Proceedings - International Symposium on Biomedical Imaging",

publisher = "IEEE Computer Society",

pages = "183--187",

booktitle = "2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017",

}

Li, H, Yin, Z & Xu, Y 2017, Automated vesicle fusion detection using Convolutional Neural Networks. in 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017., 7950497, Proceedings - International Symposium on Biomedical Imaging, IEEE Computer Society, pp. 183-187, 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017, Melbourne, Australia, 04/18/17. https://doi.org/10.1109/ISBI.2017.7950497

Automated vesicle fusion detection using Convolutional Neural Networks. / Li, Haohan; Yin, Zhaozheng; Xu, Yingke.
2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017. IEEE Computer Society, 2017. p. 183-187 7950497 (Proceedings - International Symposium on Biomedical Imaging).

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

TY - GEN

T1 - Automated vesicle fusion detection using Convolutional Neural Networks

AU - Li, Haohan

AU - Yin, Zhaozheng

AU - Xu, Yingke

PY - 2017/6/15

Y1 - 2017/6/15

N2 - Quantitative analysis of vesicle-plasma membrane fusion events in the fluorescence microscopy, has been proven to be important in the vesicle exocytosis study. In this paper, we present a framework to automatically detect fusion events. First, an iterative searching algorithm is developed to extract image patch sequences containing potential events. Then, we propose an event image to integrate the critical image patches of a candidate event into a single-image joint representation as the input to Convolutional Neural Networks (CNNs). According to the duration of candidate events, we design three CNN architectures to automatically learn features for the fusion event classification. Compared on 9 challenging datasets, our proposed method showed very competitive performance and outperformed two state-of-the-arts.

AB - Quantitative analysis of vesicle-plasma membrane fusion events in the fluorescence microscopy, has been proven to be important in the vesicle exocytosis study. In this paper, we present a framework to automatically detect fusion events. First, an iterative searching algorithm is developed to extract image patch sequences containing potential events. Then, we propose an event image to integrate the critical image patches of a candidate event into a single-image joint representation as the input to Convolutional Neural Networks (CNNs). According to the duration of candidate events, we design three CNN architectures to automatically learn features for the fusion event classification. Compared on 9 challenging datasets, our proposed method showed very competitive performance and outperformed two state-of-the-arts.

KW - Convolutional neural networks

KW - Fusion event identification

KW - Vesicle exocytosis

UR - https://www.scopus.com/pages/publications/85023190902

U2 - 10.1109/ISBI.2017.7950497

DO - 10.1109/ISBI.2017.7950497

M3 - Conference contribution

AN - SCOPUS:85023190902

T3 - Proceedings - International Symposium on Biomedical Imaging

SP - 183

EP - 187

BT - 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017

PB - IEEE Computer Society

T2 - 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017

Y2 - 18 April 2017 through 21 April 2017

ER -

Automated vesicle fusion detection using Convolutional Neural Networks

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Keywords

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