AAM-based segmentation for imaging cardiac electrophysiology
Objectives. Activation time (AT) imaging from electrocardiographic (ECG) mapping data has been developing for several years. By coupling 4-dimensional volume data (31) + time) the electrical sequence can be computed non-invasively. In this paper an approach for extracting the ventricular and atrial blood mosses for structurally normal hearts by using cine-gated short-axis data obtained via magnetic resonance imaging (MRI) is introduced. Methods: The blood mosses are extracted by employing Active Appearance Models (AAMs). The ventricular blood masses are segmented, applying the AAMs after providing apex cordis and base of the heart in the volume data, whereas the more complex geometry of the atria requires a more specific attempt. On account of this the atrium was divided into three divisions of appearance, where the images of the volume data in the related divisions have a maximum affinity. The first division reaches from the base of the heart to initial visibility of the upper and left lower pulmonary vein. The second division up from there to the lost occurrence and the third division from there to the end of the visibility of the right upper and lower pulmonary vein. After extracting the cardiac blood mosses the result gets triangulated and remeshed for activation time imaging. Results: With this method the cardiac models of eight patients were extracted and the AT imaging approach was applied to single-beat ECG data of atrial and ventricular depolorization. Conclusion: The advantage of the proposed AAM approach is that only a few initial parameters have to be set. Therefore, the approach can be integrated into a processing pipeline that works semi-automotically. The extracted models can be used for further investigations.
segmentation active appearance models image processing medical imaging