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.

Meeting Name

IEEE International Symposium on Biomedical Imaging, ISBI 2017 (2017: Apr. 18-21, Melbourne, Australia)


Computer Science

Research Center/Lab(s)

Intelligent Systems Center

Keywords and Phrases

Vesicle Exocytosis; Fusion Event Identification; Convolutional Neural Networks

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

21 Apr 2017