Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation

This repository contains the official Pytorch implementation of Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation(accepted by MICCAI 2021 as Oral). Check the presentation in our official YouTube channel.

The repository is created by Xiao Liu, Spyridon Thermos, Alison O'Neil, and Sotirios A. Tsaftaris, as a result of the collaboration between The University of Edinburgh and Canon Medical Systems Europe. You are welcome to visit our group website: vios.s

System Requirements

• Pytorch 1.5.1 or higher with GPU support
• Python 3.7.2 or higher
• SciPy 1.5.2 or higher
• CUDA toolkit 10 or newer
• Nibabel
• Pillow
• Scikit-image
• TensorBoard
• Tqdm

Datasets

We used two datasets in the paper: Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge (M&Ms) datast and Spinal cord grey matter segmentation challenge dataset. The dataloader in this repo is only for M&Ms dataset.

Preprocessing

You need to first change the dirs in the scripts of preprocess folder. Download the M&Ms data and run split_MNMS_data.py to split the original dataset into different domains. Then run save_MNMS_2D.py to save the original 4D data as 2D numpy arrays. Finally, run save_MNMS_re.py to save the resolution of each datum.

Training

Note that the hyperparameters in the current version are tuned for BCD to A cases. For other cases, the hyperparameters and few specific layers of the model are slightly different. To train the model with 5% labeled data, run:

python train_meta.py -e 150 -c cp_dgnet_meta_5_tvA/ -t A -w DGNetRE_COM_META_5_tvA -g 0

Here the default learning rate is 4e-5. You can change the learning rate by adding -lr 0.00002 (sometimes this is better).

To train the model with 100% labeled data, try to change the training parameters to:

k_un = 1
k1 = 20
k2 = 2

The first parameter controls how many iterations you want the model to be trained with unlabaled data for every iteration of training. k1 = 20 means the learning rate will start to decay after 20 epochs and k2 = 2 means it will check if decay learning every 2 epochs.

Also, change the ratio k=0.05 (line 221) to k=1 in mms_dataloader_meta_split.py.

Then, run:

python train_meta.py -e 80 -c cp_dgnet_meta_100_tvA/ -t A -w DGNetRE_COM_META_100_tvA -g 0

Finally, when training the model, changing the resampling_rate=1.2 (line 47) in mms_dataloader_meta_split.py to 1.1 - 1.3 may cause better results. This will change the rescale ratio when preprocessing the images, which will affect the size of the anatomy of interest.

Inference

After training, you can test the model:

python inference.py -bs 1 -c cp_dgnet_meta_100_tvA/ -t A -g 0

This will output the DICE and Hausdorff results as well as the standard deviation. Similarly, changing the resampling_rate=1.2 (line 47) in mms_dataloader_meta_split_test.py to 1.1 - 1.3 may cause better results.

Qualitative results

Citation

@inproceedings{liu2021semi,
  title={Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation},
  author={Liu, Xiao and Thermos, Spyridon and O’Neil, Alison and Tsaftaris, Sotirios A},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={307--317},
  year={2021},
  organization={Springer}
}

Acknowlegement

Part of the code is based on SDNet, MLDG, medical-mldg-seg and Pytorch-UNet.

License

All scripts are released under the MIT License.