Automated Segmentation of the Left Ventricle in Cardiac MRI

We present a fully automated deformable model technique for myocardium segmentation in 3D MRI. Loss of signal due to blood flow, partial volume effects and significant variation of surface grey value appearance make this a difficult problem. We integrate various sources of prior knowledge learned from annotated image data into a deformable model. Inter-individual shape variation is represented by a statistical point distribution model, and the spatial relationship of the epi- and endocardium is modeled by adapting two coupled triangular surface meshes. To robustly accommodate variation of grey value appearance around the myocardiac surface, a prior parametric spatially varying feature model is established by classification of grey value surface profiles. Quantitative validation of 60 end-diastolic 3D MRI datasets demonstrates accuracy and robustness, with 1.28±0.81 mm mean deviation from manual segmentation. We investigate the extension to 4D by incorporating a constraint on the allowed deformation based on a learned example and show illustrative results for 4D MRI.

[1]  Jürgen Weese,et al.  Shape Constrained Deformable Models for 3D Medical Image Segmentation , 2001, IPMI.

[2]  P. Thomas Fletcher,et al.  Deformable m-rep segmentation of object complexes , 2002, Proceedings IEEE International Symposium on Biomedical Imaging.

[3]  Timothy F. Cootes,et al.  Active Appearance Models , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Daniel Rueckert,et al.  Atlas-Based Segmentation and Tracking of 3D Cardiac MR Images Using Non-rigid Registration , 2002, MICCAI.

[5]  N. Duta,et al.  Segmentation of the left ventricle in cardiac MR images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[6]  Alejandro F. Frangi,et al.  Three-dimensional modeling for functional analysis of cardiac images, a review , 2001, IEEE Transactions on Medical Imaging.

[7]  Jürgen Weese,et al.  Automated 3-D PDM construction from segmented images using deformable models , 2003, IEEE Transactions on Medical Imaging.

[8]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[9]  Demetri Terzopoulos,et al.  Deformable models in medical image analysis: a survey , 1996, Medical Image Anal..

[10]  Robert T. Schultz,et al.  Segmentation and Measurement of the Cortex from 3D MR Images , 1998, MICCAI.

[11]  Milan Sonka,et al.  Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images , 2001, IEEE Transactions on Medical Imaging.

[12]  Johan Montagnat,et al.  Space and Time Shape Constrained Deformable Surfaces for 4D Medical Image Segmentation , 2000, MICCAI.