/semantically-guided

weakly supervised learning for image registration

Primary LanguageJupyter Notebook

Semantically-guided Large Deforamtion Estimation with Deep Netowrks

Code

Example code for "Semantically-Guided Large Deformation Estimationwith Deep Networks"

(Tested with Pytorch version 1.3.1, cudnn version 7.6.03)

Datasets

Datasets used in the experiments:

[1] Brandon M. Smith, Li Zhang, Jonathan Brandt, Zhe Lin, Jianchao Yang. Exemplar-Based Face Parsing, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June, 2013.

[2] O. Bernard, A. Lalande, C. Zotti, F. Cervenansky, et al. "Deep Learning Techniques for Automatic MRI Cardiac Multi-structures Segmentation and Diagnosis: Is the Problem Solved ?" in IEEE Transactions on Medical Imaging, vol. 37, no. 11, pp. 2514-2525, Nov. 2018 doi: 10.1109/TMI.2018.2837502

Training dataset for demo can be downloaded here.

Examples

Example results of our approach on Helen dataset. (Top) reference image with the ground truth reference labels, (middle) target image with warped ground truth reference labels, (bottom) warped reference images.