Analysis of echocardiographic image sequences to study left ventricular performance
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University of Tlemcen
Abstract
Cardiovasculardiseasesarepathologiesthataffecttheheartandbloodvessels.According
to theworldhealthorganization,theyaretheleadingcauseofmortalityworldwide.
Early diagnosisofcardiacfunctiondisordersiscrucialinreducingthemortalityrate.
The LeftVentricle(LV)isavitalcomponentofthecardiovascularsystemandplaysa
significantroleinbloodcirculation.Severalclinicalparameterscanbeestimatedfrom
the LVstructureduringcardiovascularexamstoensurereliablediagnoses,includingleft
ventricularvolumesandejectionfraction.
Variouscardiacimagingmodalitiesallowvisualizationoftheleftventricularcavity.
Echocardiographyisthemostwidelyusedtechniquebycardiologistsinroutineclinical
practice duetoitsmanyadvantages.Theprimarymethodforestimatingclinicalpa-
rameters isLVsurfacesegmentationfrom2Dechocardiographicimagesequences.The
accurate evaluationoftheLVchamber’sfunctionreliesonthequalityofthesegmentation
results. However,LVmanualdelineationbycardiologistsisdifficult,time-consuming,and
imprecise duetothelowqualityofechocardiographicimages.Therefore,thereisaneed
to automaticallysegmenttheLVfromechocardiographicimagesequencestoovercome
these challenges.
In thisthesis,ourobjectiveistodevelopafullyautomaticsegmentationframework
based ondeeplearningtechniquestoassessLVperformanceusingechocardiographicim-
ages. Wetestedtheeffectivenessoftheproposedapproachesbycomparingtheobtained
results withgroundtruthdataandexistingstate-of-the-artmethodsinthisfield.The
results aresatisfactory,underliningthesignificantpotentialofautomatedtechniquesfor
echocardiographicimageanalysistohelpcardiologistsintheirdailyclinicalpractice.