Fully automatic cardiac segmentation from 3D CTA data: A multi-atlas based approach
Computed tomography angiography (CTA), a non-invasive imaging technique, is becoming increasingly popular for cardiac examination, mainly due to its superior spatial resolution compared to MRI. This imaging modality is currently widely used for the diagnosis of coronary artery disease (CAD) but it is not commonly used for the diagnosis of ventricular and atrial function. In this paper, we present a fully automatic method for segmenting the whole heart (i.e. the outer surface of the myocardium) and cardiac chambers from CTA datasets. Cardiac chamber segmentation is particularly valuable for the extraction of ventricular and atrial functional information, such as stroke volume and ejection fraction. With our approach, we aim to improve the diagnosis of CAD by providing functional information extracted from the same CTA data, thus not requiring additional scanning. In addition, the whole heart segmentation method we propose can be used for visualization of the coronary arteries and for obtaining a region of interest for subsequent segmentation of the coronaries, ventricles and atria. Our approach is based on multi-atlas segmentation, and performed within a non-rigid registration framework. A leave-one-out quantitative validation was carried out on 8 images. The method showed a high accuracy, which is reflected in both a mean segmentation error of 1.05±1.30 mm and an average Dice coefficient of 0.93. The robustness of the method is demonstrated by successfully applying the method to 243 additional datasets, without any significant failure.
|cardiac segmentation, Computed Tomography Angiography (CTA), multi-atlas based segmentation, registration|
|Medical Imaging 2010: Image Processing|
|Organisation||Department of Radiology|
Kirisli, H.A, Schaap, M, Klein, S, Neefjes, L.A.E, Weustink, A.C, van Walsum, T.W, & Niessen, W.J. (2010). Fully automatic cardiac segmentation from 3D CTA data: A multi-atlas based approach. Presented at the Medical Imaging 2010: Image Processing. doi:10.1117/12.838370