一种基于LSTM神经网络的荧光寿命还原算法-豆柴文库

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一种基于LSTM神经网络的荧光寿命还原算法
Title:AFluorescenceLifetimeReconstructionAlgorithmbasedonLSTMNeuralNetworks
Abstract:
Fluorescencelifetimeimaging(FLIM)isavaluabletechniqueinmanyfields,suchasbiology,medicine,andmaterialsscience.Itprovidesimportantinformationaboutmoleculardynamics,proteininteractions,andcellularprocesses.However,FLIMdataanalysisischallengingduetonoise,signaldegradation,andlimitedphotoncounts.Inthispaper,weproposeanovelalgorithmbasedonLongShort-TermMemory(LSTM)neuralnetworksforfluorescencelifetimereconstruction.TheLSTMnetworkiscapableofcapturingthetemporaldependenciesinFLIMdata,andthusimprovingtheaccuracyoflifetimeestimation.Experimentalresultsdemonstratethattheproposedalgorithmoutperformstraditionalmethodsintermsofaccuracyandrobustness.
1.Introduction
Fluorescencelifetimeisdefinedastheaveragetimeafluorophorespendsintheexcitedstatebeforereturningtothegroundstate.Itisinfluencedbyvariousfactorssuchastemperature,polarity,pHlevel,andmolecularinteractions.FLIMisanon-invasiveimagingtechniquethatcanmeasurethefluorescencelifetimeateachpixelofanimage.Traditionally,FLIMdataanalysisinvolvescurvefittingandmathematicalmodeling,whicharecomputationallyexpensiveandpronetoerrors.Therefore,thereisaneedfornovelalgorithmsthatcanaccuratelyandefficientlyreconstructfluorescencelifetimes.
2.RelatedWork
SeveralalgorithmshavebeenproposedforFLIMdataanalysis,includingthephasoranalysis,maximumlikelihoodestimation,andtime-domainfittingmethods.Whilethesemethodshaveshownsuccessincertainscenarios,theymaystrugglewithnoisydata,complexfluorophoredistributions,andlongacquisitiontimes.Additionally,theyoftenrequiremanualtuningofparametersandassumptionsaboutthenatureoftheunderlyingfluorescencedecay.Toovercometheselimitations,weproposeadata-drivenapproachbasedonLSTMneuralnetworks.
3.Methodology
Theproposedalgorithmconsistsoftwomainstages:trainingandtesting.Duringthetrainingstage,asetofFLIMdataisusedtotraintheLSTMnetwork.Eachinputsequenceconsistsofatemporalseriesoffluorescenceintensitymeasurementsfromas