Automated Vesicle Fusion Detection using Convolutional Neural Networks
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., "Automated Vesicle Fusion Detection using Convolutional Neural Networks," Proceedings of the International Symposium on Biomedical Imaging (2015, Melborne, Australia), pp. 183-187, IEEE Computer Society, Jun 2017.
The definitive version is available at http://dx.doi.org/10.1109/ISBI.2017.7950497
International Symposium on Biomedical Imaging (2015: Apr. 18-21, Melborne, Australia)
Keywords and Phrases
Cell Membranes; Fluorescence Microscopy; Iterative Methods; Medical Imaging; Neural Networks; Competitive Performance; Convolutional Neural Network; Event Classification; Event Identification; Image Patches; Iterative Searching; Vesicle Exocytosis; Vesicle Fusion; Convolution; Fusion Event Identification
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Article - Conference proceedings
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