4D shape priors for a level set segmentation of the left myocardium in SPECT sequences.
We develop a 4D (3D plus time) statistical shape model for implicit level set based shape representations. To this end, we represent hand segmented training sequences of the left ventricle by respective 4-dimensional embedding functions and approximate these by a principal component analysis. In contrast to recent 4D models on explicit shape representations, the implicit shape model developed in this work does not require the computation of point correspondences which is known to be quite challenging, especially in higher dimensions. Experimental results on the segmentation of SPECT sequences of the left myocardium confirm that the 4D shape model outperforms respective 3D models, because it takes into account a statistical model of the temporal shape evolution.
Kohlberger
Timo
T
Siemens Corporate Research, Inc., Imaging and Visualization Department, Princeton, NJ, USA. Timo.Kohlberger@siemens.com
Cremers
Daniel
D
Rousson
Mikaël
M
Ramaraj
Ramamani
R
Funka-Lea
Gareth
G
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D004562:Electrocardiography / Q000379:methods; D006352:Heart Ventricles / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008955:Models, Cardiovascular; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D013997:Time Factors; D015899:Tomography, Emission-Computed, Single-Photon / Q000379:methods*; D018487:Ventricular Dysfunction, Left / Q000000981:diagnostic imaging*