Segmentation of cardiac MR volume data using 3D active appearance models

Abstract

Active Appearance Models (AAMs) are useful for the segmentation of cardiac MR images since they exploit prior knowledge about the cardiac shape and image appearance. However, traditional AAMs only process 2D images, not taking into account the 3D data inherent to MR. This paper presents a novel, true 3D Active Appearance Model that models the intrinsic 3D shape and image appearance of the left ventricle in cardiac MR data. In 3D-AAM, shape and appearance of the Left Ventricle (LV) is modeled from a set of expert drawn contours. The contours are then resampled to a manually defined set of landmark points, and subsequently aligned. Appearance variations in both shape and texture are captured using Principal Component Analysis (PCA) on the training set. Segmentation is achieved by minimizing the model appearance-to-target differences by adjusting the model eigen-coefficients using a gradient descent approach. The clinical potential of the 3D-AAM is demonstrated in short-axis cardiac magnetic resonance (MR) images. The method's performance was assessed by comparison with manually-identified independent standards in 56 clinical MR sequences. The method showed good agreement with the independent standards using quantitative indices such as border positioning errors, endo- and epicardial volumes, and left ventricular mass. The 3D AAM method shows high promise for successful segmentation of three-dimensional images in MR.