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.

Meeting Name

International Symposium on Biomedical Imaging (2015: Apr. 18-21, Melborne, Australia)

Department(s)

Computer Science

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

International Standard Book Number (ISBN)

978-1509011711

International Standard Serial Number (ISSN)

1945-7928

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2017 IEEE Computer Society, All rights reserved.

Publication Date

01 Jun 2017

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