4D segmentation of cardiac data using active surfaces with spatiotemporal shape priors
Amer Abufadel, Tony Yezzi, Ronald W. Schafer · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
We present a 4D spatiotemporal segmentation algorithm based on the Mumford-Shah functional coupled with shape priors. When used in a clinical setting, our algorithm could greatly alleviate the time that clinicians must spend working with the acquired data to manually retrieve diagnostically meaningful measurements. The advantage of the 4D algorithm is that segmentation occurs in both space and time simultaneously, improving accuracy and robustness over existing 2D and 3D methods. The segmentation contour or hyper-surface is a zero level set function in 4D space that exploits the coherence within continuous regions not only between spatial slices, but between consecutive time samples as well. Shape priors are incorporated into the segmentation to limit the result to a known shape. Variations in shape are computed using principal component analysis (PCA), of a signed distance representation of the training data derived from manual segmentation of 18 carefully selected data sets. The automatic segmentation occurs by manipulating the parameters of this signed distance representation to minimize a predetermined energy functional. Several tests are presented to show the consistency and accuracy of the novel automatic 4D segmentation process.