IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Registration in Implicit Spaces Using Information Theory and Free Form Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symmetric Log-Domain Diffeomorphic Registration: A Demons-Based Approach
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
A shape-navigated image deformation model for 4D lung respiratory motion estimation
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Contributions to 3D diffeomorphic atlas estimation: application to brain images
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
MICCAI'10 Proceedings of the 2010 international conference on Prostate cancer imaging: computer-aided diagnosis, prognosis, and intervention
Motion compensated SLAM for image guided surgery
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
A log-euclidean framework for statistics on diffeomorphisms
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Logarithm odds maps for shape representation
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
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This paper describes a novel approach for model based estimation of a dense deformation field utilizing an implicit representation of shape changes. Unlike existing methods based on the Point Distribution Model (PDM), the proposed method is not affected by an incorrect point correspondence which is a major limiting factor in practical applications of the PDM with clinical data. The proposed method uses regression between parametric representations of pelvic organs' shape and corresponding dense displacement field parameterized by the stationary vector field. The regression function is learned based on the training data sets including subjects with representative organ deformations, where the inter- and intra- subject correspondences are established via the log-Euclidean diffeomorphic formulation. The evaluation of the proposed method is conducted both on synthetic examples to provide systematic experimental evidence of correctness of the implicit shape representation for shape-driven prediction of the deformation field and, real MRI data to show accuracy in terms of deformation and prostate position prediction. The results show an increased robustness of the proposed framework in comparison to PDM approaches and suggest potential of its application for adaptive radiation therapy of prostate.