Learning optical flow propagation strategies using random forests for fast segmentation in dynamic 2D & 3D echocardiography

Fast segmentation of the left ventricular (LV) myocardium in 3D+time echocardiographic sequences can provide quantitative data of heart function that can aid in clinical diagnosis and disease assessment. We present an algorithm for automatic segmentation of the LV myocardium in 2D and 3D sequences which employs learning optical flow (OF) strategies. OF motion estimation is used to propagate single-frame segmentation results of the Random Forest classifier from one frame to the next. The best strategy for propagating between frames is learned on a per-frame basis. We demonstrate that our algorithm is fast and accurate. We also show that OF propagation increases the performance of the method with respect to the static baseline procedure, and that learning the best OF propagation strategy performs better than single-strategy OF propagation.