title: A shape-space-based approach to tracking myocardial borders and quantifying regional left-ventricular function applied in echocardiography.
abstract: This paper presents a new semi-automatic method for quantifying regional heart function from two-dimensional echocardiography. In the approach, we first track the endocardial and epicardial boundaries using a new variant of the dynamic snake approach. The tracked borders are then decomposed into clinically meaningful regional parameters, using a novel interpretational shape-space motivated by the 16-segment model used in clinical practice for qualitative assessment of heart function. We show how a quantitative and automatic scoring scheme for the endocardial excursion and myocardial thickening can be derived from this. Results illustrating our approach on apical long-axis two-chamber-view data from a patient with a myocardial infarct in the apical anterior/inferior region of the heart are presented. In a case study (five patients, nine data sets) the performance of the tracking and interpretation techniques are compared with manual delineations of borders using a number of quantitative measures of regional comparison.
authors: Department of Engineering Science, University of Oxford, UK.
authors: Gary
authors: G
authors: Jacob
authors: J Alison
authors: JA
authors: Noble
authors: Christian
authors: C
authors: Behrenbruch
authors: Andrew D
authors: AD
authors: Kelion
authors: Adrian P
authors: AP
authors: Banning
mesh_terms: D000465:Algorithms; D004452:Echocardiography / Q000379:methods*; D004548:Elasticity; D006321:Heart / Q000503:physiopathology; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008955:Models, Cardiovascular; D015233:Models, Statistical; D009068:Movement; D009200:Myocardial Contraction; D009203:Myocardial Infarction / Q000000981:diagnostic imaging* / Q000503:physiopathology; D012680:Sensitivity and Specificity; D018487:Ventricular Dysfunction, Left / Q000000981:diagnostic imaging*