Abstract
Automated left ventricle (LV) boundary delineation from contrast ventriculograms (LVG) has been studied for decades. Unfortunately, no methods have ever been reported with sufficient accuracy evaluations. A new knowledge based multi-stage method to automatically delineate the LV boundary at end diastole (ED) and end systole (ES) is discussed here which has shown its robustness over a large LVG database. It makes extensive use of knowledge about LV shape and movement throughout its processing, including a regional pixel classification, a shape regression and a rejection classification. The method was trained and tested on a database of 375 studies whose ED and ES boundary have been manually traced. The cross-validated results are presented in the paper, showing that the accuracy was close to interobserver variability.
Published Version
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