BrainSurfaceTK provides a suite of tools to apply geometric deep learning techniques to brain surfaces and visualise the results in an easy-to-use web application.
To install all required packages, please setup a virtual environment as per the instructions below. This virtual environment is based on a CUDA 10.1.105 installation.
python3 -m venv venv
source venv/bin/activate
pip install -r requirements1.txt -f https://download.pytorch.org/whl/torch_stable.html
pip install -r requirements2.txt -f https://pytorch-geometric.com/whl/torch-1.5.0.html
Alternatively, for a CPU installation, please setup the virtual environment as per the instructions below. Please note that the MeshCNN model requires the CUDA based installation above.
python3 -m venv venv
source venv/bin/activate
pip install -r cpu_requirements1.txt -f https://download.pytorch.org/whl/torch_stable.html
pip install -r cpu_requirements2.txt -f https://pytorch-geometric.com/whl/torch-1.5.0.html
This server has been developed using the Django framework. We use MOD-WSGI Standalone to run an Apache server to host this webapp.
The MRI visualisation is done thanks to Nilearn and the Brain surface is displayed using VTK.js.
After following the instructions on creating a virtual environment containing all of our dependencies:
- First run this until the server is successfully running:
python startserver.py
- Next you will want to create a super user. This can be done by:
chmod 700 ./createsuperuser.sh
./createsuperuser.sh
- Next you may want to use your own original data, this can be done by overwriting the meta_data.tsv data file in
GUI/media/original/data, please take care that the column names are exactly the same and in the same order. If not, then the load data function that can be called in the admin panel will not work. - After you've created a super user, you can either run the server in development mode by running:
python startserver.py
- Alternatively you may wish to run the server in production mode. If you want others to remotely access this server, you may need to open port 8000 on your machine and please don't forget to port forward if you are using a modem. After you have done this, you can simply run:
python startserver.py prod
PointNet++ is a hierarchical neural network, which was proposed to be used on point-cloud geometric data [1] for the tasks of regression, classification, and segmentation. In this project, we apply this architecture on point-cloud representations of brain surfaces for the tasks of age regression and brain segmentation.
The run instructions differ slightly for Pointnet regression and segmentation. Please proceed to the README in models/pointnet of this repository for full information.
MeshCNN [2] is a general-purpose deep neural network for 3D triangular meshes, which can be used for tasks such as 3D shape classification or segmentation. This framework includes convolution, pooling and unpooling layers which are applied directly on the mesh edges.
The original GitHub repo and additional run instructions can be found here: https://github.com/ranahanocka/MeshCNN/
In this repository, we have made multiple modifications. These include functionality for regression, adding global features into the penultimate fully-connected layers, adding logging of test-ouput, allowing for a train/test/validation split, and functionality for new learning-rate schedulers among other features.
Place the .obj mesh data files into a folder in models/MeshCNN/datasets with the correct folder structure - below is an example of the structure. Here, brains denotes the name of the directory in models/MeshCNN/datasets which holds one directory for each class, here e.g. Male and Female. In each class, folders train, val and test hold the files.
Please additionally place a file called meta_data.tsv in the models/MeshCNN/util folder. This tab-separated file will be used to read in additional labels and features into the model. The file should contain columns participant_id and session_id, which will be concatenated to form a unique identifier of a patient's scan. This unique identifier must be used to name the data files in the datasets/ folder structure described above. E.g. a meta_data.tsv file might look like this:
participant_id session_id scan_age
CC00549XX22 100100 42.142347
The corresponding mesh data file must then be named CC00549XX22_100100.obj
Any continuous-valued columns in the meta_data.tsv file can then be used as features or labels in the regression using switches in the training file, as mentioned below.
--label scan_age
--features birth_age
From the main repository level, the model can then be trained using, e.g. for regression
./scripts/regression/MeshCNN/train_reg_brains.sh
Similarly, a pre-trained model can be applied to the test set, e.g.
./scripts/regression/MeshCNN/test_reg_brains.sh
GCNN [3] is a Graph Convolution Neural Network and uses the Deep Graph Library (DGL) [4] implementation of a Graph Convolutional layer.
Here is an example on how to run the model:
python -u models/gNNs/basicgcntrain.py /path_to/meshes False all --batch_size 32 --save_path ../tmp_save --results ./results
Please note that the BrainNetworkDataset will convert the vtk PolyData and save them as DGL graphs in a user-specified folder. This is don't because the conversion process can be a bit slow and for multiple experiments, this becomes beneficial.
If you use this code useful for your research, please cite our paper:
@article{vosylius2020geometric,
title={Geometric Deep Learning for Post-Menstrual Age Prediction based on the Neonatal White Matter Cortical Surface},
author={Vitalis Vosylius and Andy Wang and Cemlyn Waters and Alexey Zakharov and Francis Ward and Loic Le Folgoc and
John Cupitt and Antonios Makropoulos and Andreas Schuh and Daniel Rueckert and Amir Alansary},
year={2020},
eprint={2008.06098},
archivePrefix={arXiv},
primaryClass={cs.CV}
}