A knowledge-based boundary delineation system for contrast ventriculograms

Automated left-ventricle (LV) boundary delineation from contrast ventriculograms has been studied for decades. Unfortunately, no accurate methods have ever been reported. A new knowledge based multistage method to automatically delineate the LV boundary at end diastole (ED) and end systole (ES) is discussed in this paper. It has a mean absolute boundary error of about 2 mm and an associated ejection fraction error of about 6%. The method makes extensive use of knowledge about LV shape and movement. The processing includes a multi-image pixel region classification, shape regression, and rejection classification. The method was trained and cross-validated tested on a database of 375 studies whose ED and ES boundary had been manually traced as the ground truth. The cross-validated results presented in this paper show that the accuracy is close to and slightly above the interobserver variability.