Automatic 3D ASM Construction via Atlas-Based Landmarking and Volumetric Elastic Registration
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Adaptive Stochastic Gradient Descent Optimisation for Image Registration
International Journal of Computer Vision
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Riemannian elasticity: a statistical regularization framework for non-linear registration
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
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Deformable registration is prone to errors when it involves large and complex deformations, since the procedure can easily end up in a local minimum. To reduce the number of local minima, and thus the risk of misalignment, regularization terms based on prior knowledge can be incorporated in registration. We propose a regularization term that is based on statistical knowledge of the deformations that are to be expected. A statistical model, trained on the shapes of a set of segmentations, is integrated as a penalty term in a free-form registration framework. For the evaluation of our approach, we perform inter-patient registration of MR images, which were acquired for planning of radiation therapy of cervical cancer. The manual delineations of structures such as the bladder and the clinical target volume are available. For both structures, leave-one-patient-out registration experiments were performed. The propagated atlas segmentations were compared to the manual target segmentations by Dice similarity and Hausdorff distance. Compared with registration without the use of statistical knowledge, the segmentations were significantly improved, by 0.1 in Dice similarity and by 8mm Hausdorff distance on average for both structures.