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.
H. Li et al., "A Deep Learning Framework for Automated Vesicle Fusion Detection," Proceedings of the IEEE International Symposium on Biomedical Imaging (2017, Melbourne, Australia), Institute of Electrical and Electronics Engineers (IEEE), Apr 2017.
IEEE International Symposium on Biomedical Imaging, ISBI 2017 (2017: Apr. 18-21, Melbourne, Australia)
Intelligent Systems Center
Keywords and Phrases
Vesicle Exocytosis; Fusion Event Identification; Convolutional Neural Networks
Article - Conference proceedings
© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
21 Apr 2017