Search results for " ultrasound images"

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The size of juxtaluminal hypoechoic area in ultrasound images of asymptomatic carotid plaques predicts the occurrence of stroke

2013

Abstract OBJECTIVE: To test the hypothesis that the size of a juxtaluminal black (hypoechoic) area (JBA) in ultrasound images of asymptomatic carotid artery plaques predicts future ipsilateral ischemic stroke. METHODS: A JBA was defined as an area of pixels with a grayscale value 10 mm(2) (P 8 mm(2)) was still significant after adjusting for other plaque features known to be associated with increased risk, including stenosis, grayscale median, presence of discrete white areas without acoustic shadowing indicating neovascularization, plaque area, and history of contralateral TIA or stroke. Plaque area and grayscale median were not significant. Using the significant variables (stenosis, discr…

Asymptomatic carotid plaqueMaleTime Factorsmedicine.medical_treatmentCarotid endarterectomyKaplan-Meier EstimateSeverity of Illness Indexasymptomatic carotid artery stenosis; hypoecoic area; StrokeRisk FactorsCarotid StenosisUltrasonography Doppler ColorProspective cohort studyStrokeAged 80 and overNeovascularization Pathologichypoecoic areaMiddle AgedPrognosisPlaque AtheroscleroticEuropeStrokeIschemic Attack TransientPredictive value of testsFemaleRadiologymedicine.symptomjuxtaluminal hypoechoic area ultrasound images asymptomatic carotid plaques strokeCardiology and Cardiovascular MedicineCarotid Artery InternalAdultmedicine.medical_specialtyAsymptomaticRisk AssessmentAsymptomatic carotid plaque; Brain ischemia; Stroke; EchographyBrain ischemiaPredictive Value of Testsasymptomatic carotid artery stenosimedicineHumanscardiovascular diseasesAgedProportional Hazards Modelsbusiness.industryProportional hazards modelmedicine.diseaseAcoustic shadowSettore MED/11 - Malattie Dell'Apparato CardiovascolareStenosisROC CurveAsymptomatic DiseasesLinear ModelsSurgeryasymptomatic carotid artery stenosisEchographybusinessFollow-Up Studies
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Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks

2021

[EN] Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is laborious, requires expertise and, even then, the inter-observer variability remains high. In this paper, a robust, accurate and generalizable model for Magnetic Resonance (MR) and three-dimensional (3D) Ultrasound (US) prostate image segmentation is proposed. It uses a densenet-resnet-based Convolutional Neural Network (CNN) combined with techniques such as deep supervision, checkpoint ensembling and Neural Resolution Enhancement. The MR prostate segmentation model was tra…

Computer scienceMR prostate imagingUS prostate imagingINGENIERIA MECANICAconvolutional neural networklcsh:TechnologyConvolutional neural network030218 nuclear medicine & medical imaginglcsh:Chemistry03 medical and health sciences0302 clinical medicinemedicineGeneral Materials Sciencelcsh:QH301-705.5Instrumentation030304 developmental biologyFluid Flow and Transfer Processes0303 health sciencesmedicine.diagnostic_testlcsh:Tbusiness.industryProcess Chemistry and TechnologyConvolutional Neural NetworksUltrasoundResolution (electron density)General EngineeringMagnetic resonance imagingPattern recognitionProstate Segmentationlcsh:QC1-999Computer Science ApplicationsNeural resolution enhancementlcsh:Biology (General)lcsh:QD1-999lcsh:TA1-2040Christian ministryArtificial intelligencelcsh:Engineering (General). Civil engineering (General)Magnetic Resonance and Ultrasound Imagesbusinesslcsh:PhysicsProstate segmentationApplied Sciences
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