Use of artificial neural networks to identify and analyze polymerized actin-based cytoskeletal structures in 3D confocal images  

作　　者：Doyoung Park 

机构地区：[1]Department of Mathematics,Computer&Information Science,State University of New York at Old Westbury,Old Westbury,New York,11568,USA

出　　处：《Quantitative Biology》2023年第3期306-319,共14页定量生物学（英文版）

摘　　要：Background:Living cells need to undergo subtle shape adaptations in response to the topography of their substrates.These shape changes are mainly determined by reorganization of their internal cytoskeleton,with a major contribution from filamentous(F)actin.Bundles of F-actin play a major role in determining cell shape and their interaction with substrates,either as“stress fibers,”or as our newly discovered“Concave Actin Bundles”(CABs),which mainly occur while endothelial cells wrap micro-fibers in culture.Methods:To better understand the morphology and functions of these CABs,it is necessary to recognize and analyze as many of them as possible in complex cellular ensembles,which is a demanding and time-consuming task.In this study,we present a novel algorithm to automatically recognize CABs without further human intervention.We developed and employed a multilayer perceptron artificial neural network(“the recognizer”),which was trained to identify CABs.Results:The recognizer demonstrated high overall recognition rate and reliability in both randomized training,and in subsequent testing experiments.Conclusion:It would be an effective replacement for validation by visual detection which is both tedious and inherently prone to errors.

关 键 词：Concave Actin Bundles artificial neural network recognizer planar actin distribution 3D probability density estimation cytoskeletal structures 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]