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http://dspace1.univ-tlemcen.dz/handle/112/22631
Titre: | Analysis of echocardiographic image sequences to study left ventricular performance |
Auteur(s): | BELFILALI, Hafida |
Mots-clés: | Left ventricle;Echocardiography;Segmentation;Echocardiographicimage analysis; Deeplearning;U-Netarchitecture;Attentionmechanism;Transferlearning |
Date de publication: | 25-sep-2023 |
Editeur: | University of Tlemcen |
Résumé: | 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. |
URI/URL: | http://dspace1.univ-tlemcen.dz/handle/112/22631 |
Collection(s) : | Doctorat en GBM |
Fichier(s) constituant ce document :
Fichier | Description | Taille | Format | |
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Analysis_of_echocardiographic_image_sequences_to_study_left_ventricular_performance.pdf | 12,35 MB | Adobe PDF | Voir/Ouvrir |
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