Active shape models—their training and application
Computer Vision and Image Understanding
Multi-classifier framework for atlas-based image segmentation
Pattern Recognition Letters
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Efficient Large Deformation Registration via Geodesics on a Learned Manifold of Images
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
IEEE Transactions on Image Processing
A survey of shaped-based registration and segmentation techniques for cardiac images
Computer Vision and Image Understanding
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Automatic segmentation of cardiac MRI is an important but challenging task in clinical study of cardiac morphology. Recently, fusing segmentations from multiple classifiers has been shown to achieve more accurate results than a single classifier. In this work, we propose a new strategy, MUltiple Path Propagation and Segmentation (MUPPS), in contrast with the currently widely used multi-atlas propagation and segmentation (MAPS) scheme. We showed that MUPPS outperformed the standard MAPS in the experiment using twenty-one in vivo cardiac MR images. Furthermore, we studied and compared different path selection strategies for the MUPPS, to pursue an efficient implementation of the segmentation framework. We showed that the path ranking scheme using the image similarity after an affine registration converged faster and only needed eleven classifiers from the atlas repository. The fusion of eleven propagation results using the proposed path ranking scheme achieved a mean Dice score of 0.911 in the whole heart segmentation and the highest gain of accuracy was obtained from myocardium segmentation.