4D segmentation of cardiac data using active surfaces with spatiotemporal shape priors

Amer Abufadel; Tony Yezzi; Ronald Schafer

doi:10.1117/12.715098

28 February 2007 4D segmentation of cardiac data using active surfaces with spatiotemporal shape priors

Amer Abufadel, Tony Yezzi, Ronald Schafer

Proceedings Volume 6498, Computational Imaging V; 64980D (2007) https://doi.org/10.1117/12.715098
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

Abstract

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.

Citation Download Citation

Amer Abufadel, Tony Yezzi, and Ronald Schafer "4D segmentation of cardiac data using active surfaces with spatiotemporal shape priors", Proc. SPIE 6498, Computational Imaging V, 64980D (28 February 2007); https://doi.org/10.1117/12.715098

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