Int J Comput Assist Radiol Surg - Automatic model-based contour detection of left ventricle myocardium from cardiac CT images.

Tópicos

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Resumo

RPOSE: For accurate evaluation of myocardial perfusion on computed tomography images, precise identification of the myocardial borders of the left ventricle (LV) is mandatory. In this article, we propose a method to detect the contour of LV myocardium automatically and accurately.METHODS: Our detection method is based on active shape model. For precise detection, we estimate the pose and shape parameters separately by three steps: LV coordinate system estimation, myocardial shape estimation, and transformation. In LV coordinate system estimation, we detect heart features followed by the entire LV by introducing machine-learning approach. Since the combination of two types feature detection covers the LV variation, such as pose or shape, we can estimate the LV coordinate system robustly. In myocardial shape estimation, we minimize the energy function including pattern error around myocardium with adjustment of pattern model to input image using estimated concentration of contrast dye. Finally, we detect LV myocardial contours in the input images by transforming the estimated myocardial shape using the matrix composed of the vectors calculated by the LV coordinate system estimation.RESULTS: In our experiments with 211 images from 145 patients, mean myocardial contours point-to-point errors for our method as compared to ground truth were 1.02?mm for LV endocardium and 1.07?mm for LV epicardium. The average computation time was 2.4?s (on a 3.46?GHz processor with 2-multithreading process).CONCLUSIONS: Our method achieved accurate and fast myocardial contour detection which may be sufficient for myocardial perfusion examination.

Resumo Limpo

rpose accur evalu myocardi perfus comput tomographi imag precis identif myocardi border left ventricl lv mandatori articl propos method detect contour lv myocardium automat accuratelymethod detect method base activ shape model precis detect estim pose shape paramet separ three step lv coordin system estim myocardi shape estim transform lv coordin system estim detect heart featur follow entir lv introduc machinelearn approach sinc combin two type featur detect cover lv variat pose shape can estim lv coordin system robust myocardi shape estim minim energi function includ pattern error around myocardium adjust pattern model input imag use estim concentr contrast dye final detect lv myocardi contour input imag transform estim myocardi shape use matrix compos vector calcul lv coordin system estimationresult experi imag patient mean myocardi contour pointtopoint error method compar ground truth mm lv endocardium mm lv epicardium averag comput time s ghz processor multithread processconclus method achiev accur fast myocardi contour detect may suffici myocardi perfus examin

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