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Fetal brain segmentation with MONAI DynUNet

MONAIfbs (MONAI Fetal Brain Segmentation) is a Pytorch-based toolkit to train and test deep learning models for automated fetal brain segmentation of HASTE-like MR images. The toolkit was developed within the GIFT-Surg research project, and takes advantage of MONAI, a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging.

A pre-trained dynUNet model is provided and can be directly used for inference on new data using the script src/inference/monai_dynunet_inference.py. Alternatively, the script fetal_brain_seg.py provides the same inference functionality within an appropriate interface to be used within the NiftyMIC package and by the executable command niftymic_segment_fetal_brains. See the sections Inference and Use within NiftyMIC below.

More information about MONAI dynUNet can be found here. This deep learning pipeline is based on the nnU-Net self-adapting framework for U-Net-based medical image segmentation.

Contact information

This package was developed by Marta B.M. Ranzini at the Department of Surgical and Interventional Sciences, King's College London (KCL) (2020). If you have any questions or comments, please open an issue on GitHub or contact Prof Tom Vercauteren at tom.vercauteren@kcl.ac.uk.

Important note

Please make sure you download the pre-trained model for inference and add it in the MONAIfbs folder as follows <path_to_MONAIfbs>/monaifbs/models/checkpoint_dynUnet_DiceXent.pt. You can either download it manually from the webpage or you can use the zenodo_get tool from command line:

pip install zenodo-get
zenodo_get 10.5281/zenodo.4282679
tar xvd models.tar.gz
mv models <path_to_MONAIfbs>/monaifbs/

Alternatively, you can store the model at a different location, but update the inference config file with the right path to the model to load.

Installation

After installing git lfs, clone the repository locally using
git clone https://github.com/gift-surg/MONAIfbs.git

Change to the downloaded directory and install all Python and Pytorch dependencies by running the following commands sequentially:
pip install -r requirements.txt
pip install -e .

Note: MONAI and monaifbs require Python versions >= 3.6.

Note: A CUDA compatible GPU is recommended for training. Inference can be run both on GPU or on CPU.

Training

A python script was developed to train a dynUNet model using MONAI. The dynUNet is based on the nnU-Net approach, which employs a series of heuristic rules to determine the optimal kernel sizes, strides and network depth from the training set.

The available script trains a 2D dynUNet by randomly sampling 2D slices from the training set. By default, Dice + Cross Entropy is used as loss function (other options are available).
Validation during training is also performed: a whole-volume validation strategy is applied (using a 2D sliding-window approach throughout each 3D image) and Mean 3D Dice Score over the validation set is used as metric for best model selection.

Setting the training to run

To run the training with your own data, the following command can be used:

python <path_to_MONAIfbs>/monaifbs/src/train/monai_dynunet_training.py \
--train_files_list <path-to-list-of-training-files.txt> \
--validation_files_list <path-to-list-of-validation-files.txt>\
--out_folder <path-to-output-directory>

The files <path-to-list-of-training-files.txt> and <path-to-list-of-validation-files.txt> should be either .txt or .csv files storing pairs of image-segmentation filenames in each line, separated by a comma, as follows:

/path/to/file/for/subj1_img.nii.gz,/path/to/file/for/subj1_seg.nii.gz
/path/to/file/for/subj2_img.nii.gz,/path/to/file/for/subj2_seg.nii.gz
/path/to/file/for/subj3_img.nii.gz,/path/to/file/for/subj3_seg.nii.gz
...

Examples of the expected file formats are in config/mock_train_file_list_for_dynUnet_training.txt and config/mock_valid_file_list_for_dynUnet_training.txt.

See python <path_to_MONAIfbs>/monaifbs/src/train/monai_dynunet_training.py -h for help on additional input arguments.

Changing the network configurations

By default, the network will be trained with the configurations defined in config/monai_dynUnet_training_config.yml. See the file for a description of the user-defined parameters.
To change the parameter values, create your own yaml config file following the structure here. The new config file can be input as an argument when running the training as follows:

python <path_to_MONAIfbs>/monaifbs/src/train/monai_dynunet_training.py \
--train_files_list <path-to-list-of-training-files.txt> \
--validation_files_list <path-to-list-of-validation-files.txt>\
--out_folder <path-to-output-directory>
--config_file <path-to-customed-config-file.yml>

When running inference, make sure the config file for inference is also updated accordingly, otherwise the model might not be correctly reloaded (See the section Inference below).

Using the GPU

The code is optimised to be used with 1 GPU (multi-GPU computation is not supported at present). To set the GPU to use, run the command export CUDA_VISIBLE_DEVICES=<gpu_number> before running the python commands described above.

Understanding the output

The script will generate two subfolders in the indicated output directory:

folder with name formatted as Year-month-day_hours-minutes-seconds_out_postfix, which stores the results of the training. out_postix is monai_dynUnet_2D by default, but can be changed as input argument when running the training. This folder contains:
- best_valid_checkpoint_key_metric=####.pt: saved pytorch model best performing on the validation set (based on Mean 3D Dice Score)
- checkpoint_epoch=####.pth: latest saved pytorch model
- directories train and valid storing the tensorboard outputs for the training and the validation respectively
folder named persistent_cache. To speed up the computation, the script uses MONAI PersistentDataset, which pre-computes and stores to disk all the non-random pre-processing steps (pre-processing transforms outputs). This folder stores the results of these pre-computations.

Notes on the persistent cache:

The persistent cache dataset favours reusability of pre-processed data when multiple runs need to be executed (e.g. for hyperparameters tuning). To change the location of this persistent cache or to re-use a pre-existing cache, the option --cache_dir <path-to-persistent-cache> can be used in the command line for setting the training to run.
The persistent cache can take up quite some large amount of storage space, depending on the size of the training set and on the selected patch size for training (Example: with about 400 3D volumes and default patch size (418, 512), it took about 30G).
Alternate solutions to the PersistentCache exist which do not use this much storage space, but are not currently implemented in the training script. See this MONAI tutorial for more information. To integrate other MONAI Datasets into the script, change the train_ds and val_ds definitions in src/train/monai_dynunet_training.py.

Inference

Note If using the provided pre-trained model, please make sure you have downloaded it and placed it as expected in <path_to_MONAIfbs>/monaifbs/models. More details in the important Note above.

Inference can be run with the provided inference script with the following command:

python <path_to_MONAIfbs>/monaifbs/src/inference/monai_dynunet_inference.py \
--in_files <path-to-img1.nii.gz> <path-to-img2.nii.gz> ... <path-to-imgN.nii.gz> \ 
--out_folder <path-to-output-directory>

By default, this will use the provided pre-trained model and the network configuration parameters reported in this config file. If you want to specify a different (MONAI dynUNet) trained model, you can create your own config file indicating the model to load and its network configuration parameters following the provided template. Then, you can simply run inference as:

python <path_to_MONAIfbs>/monaifbs/src/inference/monai_dynunet_inference.py \
--in_files <path-to-img1.nii.gz> <path-to-img2.nii.gz> ... <path-to-imgN.nii.gz> \ 
--out_folder <path-to-output-directory> \  
--config_file <path-to-custom-config.yml>

Use within NiftyMIC (for inference)

The automated segmentation tool was developed in support to the Super-Resolution Reconstruction package NiftyMIC. By default, NiftyMIC uses monaifbs utilities to automatically generate fetal brain segmentation masks that can be used for the reconstruction pipeline. Provided the dependencies for NiftyMIC and monaifbs are installed, create the automatic fetal brain masks of HASTE-like images with the command:

niftymic_segment_fetal_brains \
--filenames \
nifti/name-of-stack-1.nii.gz \
nifti/name-of-stack-2.nii.gz \
nifti/name-of-stack-N.nii.gz \
--filenames-masks \
seg/name-of-stack-1.nii.gz \
seg/name-of-stack-2.nii.gz \
seg/name-of-stack-N.nii.gz

The interface between the niftymic package and monaifbs inference utilities is defined in fetal_brain_seg.py. Its working scheme is essentially the same as the monai_dynunet_inference.py, it simply provides a wrapper to feed the input data to the run_inference() function in monai_dynunet_inference.py. It can also be used as a standalone script for inference as follows:

python <path_to_MONAIfbs>/monaifbs/fetal_brain_seg.py \
--input_names <path-to-img1.nii.gz> <path-to-img2.nii.gz> ... <path-to-imgN.nii.gz> \ 
--segment_output_names <path-to-seg1.nii.gz> <path-to-seg2.nii.gz> ... <path-to-segN.nii.gz>

Troubleshooting

Issue with ParallelNative on MacOS

A warning message from ParallelNative is shown and the computation gets stuck. This issue appears to happen only on MacOS and is known to be linked to PyTorch DataLoader (as reported in https://github.com/pytorch/pytorch/issues/46409)

Warning message: [W ParallelNative.cpp:206] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads

When observed: MacOS, Python 3.6 and Python 3.7, running on CPU.

Solution: add OMP_NUM_THREADS=1 before the call of monaifbs scripts.
Example 1:

OMP_NUM_THREADS=1 python <path_to_MONAIfbs>/monaifbs/src/inference/monai_dynunet_inference.py \
--in_files <path-to-img1.nii.gz> <path-to-img2.nii.gz> ... <path-to-imgN.nii.gz> \ 
--out_folder <path-to-output-directory>

Example 2:

OMP_NUM_THREADS=1 niftymic_segment_fetal_brains \
--filenames \
nifti/name-of-stack-1.nii.gz \
nifti/name-of-stack-2.nii.gz \
nifti/name-of-stack-N.nii.gz \
--filenames-masks \
seg/name-of-stack-1.nii.gz \
seg/name-of-stack-2.nii.gz \
seg/name-of-stack-N.nii.gz

Disclaimer

Not intended for clinical use.

Licensing and Copyright

Copyright (c) 2020 Marta Bianca Maria Ranzini and contributors. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Other licenses may apply for dependencies.

Acknowledgements

This work is part of the GIFT-Surg project and is funded by the Innovative Engineering for Health award (Wellcome Trust [WT101957] and EPSRC [NS/A000027/1]), the Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z, NS/A000049/1]] and supported by researchers at the NIHR Biomedical Research Centre based at GSTT NHS Trust and King's College London.