Awesome
Diffusion Deformable Model (DDM)
The official source code of "diffusion deformable model (DDM)".
Implementation
A PyTorch implementation of deep-learning-based model.
- Requirements
- OS : Ubuntu / Windows
- Python 3.6
- PyTorch 1.4.0
Dataset
- In our experiment, we used 4D cardiac MR scans provided by the Automated Cardiac Diagnosis Challenge (ACDC).
- You can download the data at ACDC.
- In our experiment, we resampled all MRI scans with a voxel spacing of 1.5 x 1.5 x 3.15 mm, and saved the data with .mat.
- Examples of the data we used can be downloaded at here.
- Copy downloaded directory of "ACDC_dataset" to './data'.
- The data in the directory "data_ED_ES" have 3D MR scans at the end diastolic and at the end systolic phases, and their corresponding segmentation labels.
- The data in the directory "data_ED2ES" have 4D MR scans from the end diastolic to the end systolic phases, which is used in the test stage.
Training
- DDM_train.py is the implementation code for training the proposed diffusion deformable model.
- You can run the code through "sh train.sh" in terminal.
- All parameter settings we used are written in ./config/DDM_train.json file.
Evaluation
- DDM_test.py is the implementation code of inference for 4D temporal image generation.
- You can run the code by running "sh test.sh" in terminal.