Groupwise image registration provides an unbiased registration solution upon a population of images, which can facilitate the subsequent population analysis. However, it is generally computationally expensive for performing groupwise registration on a large set of images. To alleviate this issue, we propose to utilize a fast initialization technique for speeding up the groupwise registration. Our main idea is to generate a set of simulated brain MRI samples with known deformations to their group center. This can be achieved in the training stage by two steps. First, a set of training brain MR images is registered to their group center with a certain existing groupwise registration method. Then, in order to augment the samples, we perform PCA on the set of obtained deformation fields (to the group center) to parameterize the deformation fields. In doing so, we can generate a large number of deformation fields, as well as their respective simulated samples using different parameters for PCA. In the application stage, when given a new set of testing brain MR images, we can mix them with the augmented training samples. Then, for each testing image, we can find its closest sample in the augmented training dataset for fast estimating its deformation field to the group center of the training set. In this way, a tentative group center of the testing image set can be immediately estimated, and the deformation field of each testing image to this estimated group center can be obtained. With this fast initialization for groupwise registration of testing images, we can finally use an existing groupwise registration method to quickly refine the groupwise registration results. Experimental results on ADNI dataset show the significantly improved computational efficiency and competitive registration accuracy, compared to state-of-the-art groupwise registration methods.
|Title of host publication||Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings|
|Editors||Yinghuan Shi, Heung-Il Suk, Kenji Suzuki, Qian Wang|
|Number of pages||9|
|Publication status||Published - 2017|
|Event||8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada|
Duration: 2017 Sept 10 → 2017 Sept 10
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017|
|Period||17/9/10 → 17/9/10|
Bibliographical notePublisher Copyright:
© 2017, Springer International Publishing AG.
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)