  Recently, machine learning has been successfully applied to model-based left
ventricle (LV) segmentation. The general framework involves two stages, which
starts with LV localization and is followed by boundary delineation. Both are
driven by supervised learning techniques. When compared to previous
non-learning-based methods, several advantages have been shown, including full
automation and improved accuracy. However, the speed is still slow, in the
order of several seconds, for applications involving a large number of cases or
case loads requiring real-time performance. In this paper, we propose a fast LV
segmentation algorithm by joint localization and boundary delineation via
training explicit shape regressor with random pixel difference features. Tested
on 3D cardiac computed tomography (CT) image volumes, the average running time
of the proposed algorithm is 1.2 milliseconds per case. On a dataset consisting
of 139 CT volumes, a 5-fold cross validation shows the segmentation error is
$1.21 \pm 0.11$ for LV endocardium and $1.23 \pm 0.11$ millimeters for
epicardium. Compared with previous work, the proposed method is more stable
(lower standard deviation) without significant compromise to the accuracy.
