Simulated 3D ultrasound LV cardiac images for active shape model training

In this paper a study of 3D ultrasound cardiac segmentation using Active Shape Models (ASM) is presented. The proposed approach is based on a combination of a point distribution model constructed from a multitude of high resolution MRI scans and the appearance model obtained from simulated 3D ultrasound images. Usually the appearance model is learnt from a set of landmarked images. The significant level of noise, the low resolution of 3D ultrasound images (3D US) and the frequent failure to capture the complete wall of the left ventricle (LV) makes automatic or manual landmarking difficult. One possible solution is to use artificially simulated 3D US images since the generated images will match exactly the shape in question. In this way, by varying simulation parameters and generating corresponding images, it is possible to obtain a training set where the image matches the shape exactly. In this work the simulation of ultrasound images is performed by a convolutional approach. The evaluation of segmentation accuracy is performed on both simulated and in vivo images. The results obtained on 567 simulated images had an average error of 1.9 mm (1.73 ± 0.05 mm for epicardium and 2 ± 0.07 mm for endocardium, with 95% confidence) with voxel size being 1.1 × 1.1 × 0.7 mm. The error on 20 in vivo data was 3.5 mm (3.44 ± 0.4 mm for epicardium and 3.73 ± 0.4 mm for endocardium). In most images the model was able to approximate the borders of myocardium even when the latter was indistinguishable from the surrounding tissues.