Label-Free Physics-Informed Image Sequence Reconstruction with Disentangled Spatial-Temporal Modeling
[MICCAI, Paper]

This repository holds the experiments and models as explored in the work, "Label-Free Physics-Informed Image Sequence Reconstruction with Disentangled Spatial-Temporal Modeling." We provide guidance on training the models, as well as supplementary experiments and visualizations.

Running guidance

1. Check your cuda version. I've tested both cu101 and cu102, and both of them are able to work.
2. Python version >= 3.8 would be recommended.
3. To install the python packages, change the variable CUDA in the script req_torch_geo.sh, then run it.
4. Check the configuration under ./config/. Please use seg04.json for ODE-GCNN and seg26.json for ST-GCNN. Here are some important parameters:
   1. net_arch: the type of the network architecture
   2. ode_func_type: the function used for neural ODE
   3. seq_len: the length of the input time sequence
   4. latent_dim: the dimension of the latent feature
   5. cell_type: the type of modules for the correction step in temporal modeling
   6. nf: the number of the feature in each layer
   7. smooth: the smoothing parameter of the regularization term
5. To train the model, run the following command:

   python main.py --config seg04 --stage 1

6. To evaluate the model, run the following command:

   python main.py --config seg04 --stage 2

Supplementary material

Visualization for simulation experiments

Timelapse reconstruction for a single simulation case. Circled areas represent notable errors in reconstruction compared to ground truth.

Visualization for clinical experiments

Healthy cases

Post-infarction cases