cq615/joint-motion-estimation-and-segmentation

[MICCAI'18] Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences

Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences

Code accompanying MICCAI 2018 paper of the same title. Paper link: https://arxiv.org/abs/1806.04066

Usage

Lasagne and theano implementation of the framework.

main.py ==> main training file

test_prediction.py ==> applies joint prediction and visualisation

models ==> proposed network and layers

dataio ==> includes loading images, data_augmentation, etc

utils ==> metrics and visualisation

model ==> Model parameters

test ==> One test sample

News: Update Pytorch implementation of the work in pytorch_version.

pytorch_version ==> pytorch implementation of the models

Citation and Acknowledgement

If you use the code for your work, or if you found the code useful, please cite the following works:

Qin, C., Bai, W., Schlemper, J., Petersen, S.E., Piechnik, S.K., Neubauer, S. and Rueckert, D. Joint learning of motion estimation and segmentation for cardiac MR image sequences. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2018: 472-480.

C. Qin, W. Bai, J. Schlemper, S. Petersen, S. Piechnik, S. Neubauer and D. Rueckert. Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image. International Workshop on Machine Learning for Medical Image Reconstruction, 2018: 55-63.

Licence

This project is licensed under the terms of the MIT license.

joint-motion-estimation-and-segmentation's Issues

FCN_VGG16_sz192_joint_learn_ft_tmp.npz

May I ask if the author has a parameter file FCN_VGG16_sz192_joint_ft_tmp。