How to Trade Off between Regularization and Image Similarity in Non-rigid Registration?
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms
International Journal of Computer Vision
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 New Algorithm for Inverse Consistent Image Registration
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
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
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
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Deformable image registration is a key enabling technology for adaptive radiation therapy (ART) as it can facilitate structure segmentation as well as dose tracking and accumulation. In this work, we develop an efficient inverse-consistent diffeomorphic registration method applying the log-Euclidean formulation of diffeomorphisms. Unlike existing log-Euclidean deformable registration approaches, the proposed method deforms two images towards each other in a completely symmetric fashion during the registration optimization, which leads to higher efficiency and better accuracy in recovering large deformations. The method is applied for the automatic segmentation of daily CT images in prostate ART. To address difficulties caused by large bladder and rectum content change, we propose further improvements and combine deformable registration with model-based image segmentation. Validation results on real clinical data showed that the proposed method gives highly accurate segmentation of interested structures.