Time-continuous segmentation of cardiac MR image sequences using active appearance motion models

Active Appearance Models (AAMs) are useful for segmentation of static cardiac MR images since they exploit prior knowledge about the cardiac shape and image appearance. However, applying 2D AAMs to full cardiac cycle segmentation would require multiple models for different phases of the cardiac cycle because traditional AAMs account only for the variations within image classes and not temporal classes. This paper presents a novel 2D+time Active Appearance Motion Model (AAMM) that represents the dynamics of the cardiac cycle in combination with shape and image appearance of the heart, ensuring a time-continuous segmentation of a complete cardiac MR sequence. In AAMM, single-beat sequences are phase-normalized into sets of 2D images and the shape points and gray intensities between frames are concatenated into a shape vector and intensity vector. Appearance variations over time are captured using Principal Component Analysis on both vectors in the training set. Time-continuous segmentation is achieved by minimizing the model appearance-to-target differences by adjusting the model eigen-coefficients using gradient descent approach. In matching tests, the model shows to be robust in initial position and approximates the true segmentation very well. Large-scale clinical validation in patients is ongoing.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.