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Co-BioNet: Uncertainty-Guided Dual-Views for Semi-Supervised Volumetric Medical Image Segmentation

This repo contains the supported pytorch code and configuration files to reproduce the results of Uncertainty-Guided Dual-Views for Semi-Supervised Volumetric Medical Image Segmentation Article.

Abstract

Deep learning has led to tremendous progress in the field of medical artificial intelligence. However, training deep-learning models usually require large amounts of annotated data. Annotating large-scale datasets is prone to human biases and is often very laborious, especially for dense prediction tasks such as image segmentation. Inspired by semi-supervised algorithms that use both labelled and unlabelled data for training, we propose a dual-view framework based on adversarial learning for segmenting volumetric images. In doing so, we use critic networks to allow each view to learn from high-confidence predictions of the other view by measuring a notion of uncertainty. Furthermore, to jointly learn the dual-views and the critics, we formulate the learning problem as a min–max problem. We analyse and contrast our proposed method against state-of-the-art baselines, both qualitatively and quantitatively, on four public datasets with multiple modalities (for example, computerized topography and magnetic resonance imaging) and demonstrate that the proposed semi-supervised method substantially outperforms the competing baselines while achieving competitive performance compared to fully supervised counterparts. Our empirical results suggest that an uncertainty-guided co-training framework can make two neural networks robust to data artefacts and have the ability to generate plausible segmentation masks that can be helpful for semi-automated segmentation processes.

Link to full paper:

Published in Nature Machine Intelligence : Link

Proposed Architecture

Proposed Architecture

System requirements

Under this section, we provide details on the environmental setup and dependencies required to train/test the Co-BioNet model. This software was originally designed and run on a system running Ubuntu (Compatible with Windows 11 as well).
All the experiments are conducted on Ubuntu 20.04 Focal version with Python 3.8.
To train Co-BioNet with the given settings, the system requires a GPU with at least 40GB. All the experiments are conducted on Nvidia A40 single GPU. (Not required any non-standard hardware)
To test the model's performance on unseen Pancreas CT and LA MRI test data, the system requires a GPU with at least 4 GB.

Create a virtual environment

pip install virtualenv
virtualenv -p /usr/bin/python3.8 venv
source venv/bin/activate

Installation guide

  • Install torch :
pip3 install torch==1.10.2+cu113 torchvision==0.11.3+cu113 torchaudio==0.10.2+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
  • Install other dependencies :
pip install -r requirements.txt

Typical Install Time

This depends on the internet connection speed. It would take around 15-30 minutes to create environment and install all the dependencies required.

Dataset Preparation

The experiments are conducted on two publicly available datasets,

Pre-processed data can be found in folder data.

Figshare Project Page

All the pre-trained models, figures, evaluations, a video on how the training pipeline works, and the source code are included in this project page link

Trained Model Weights

Download trained model weights from this shared drive link, and put it under folder code/model or code_msd_brats/model

Running Demo

Demonstration is created on generating segmentation masks on a sample of unseen Pancreas CT with trained torch models on 10% and 20% Labeled Pancreas CT and Left Atrial MRI data. You can run the given python notebook in the demo folder.

Train Model

  • To train the model for Pancreas CT dataset on 10% Lableled data
cd code
nohup python train_cobionet_semi.py --dataset_name Pancreas_CT --labelnum 6 --lamda 1.0 --consistency 1.0 --mu 0.01 --t_m 0.2 --max_iteration 15000 &> pa_10_perc.out &
  • To train the model for Pancreas CT dataset on 20% Lableled data
cd code
nohup python train_cobionet_semi.py --dataset_name Pancreas_CT --labelnum 12 --lamda 1.0 --consistency 1.0 --mu 0.01 --t_m 0.2 --max_iteration 15000 &> pa_20_perc.out &
  • To train the model for Left Atrial MRI dataset on 10% Lableled data
cd code
nohup python train_cobionet_semi.py --dataset_name LA --labelnum 8 --lamda 0.7 --consistency 1.0 --mu 0.01 --t_m 0.4 --max_iteration 15000 &> la_10_perc.out &
  • To train the model for Left Atrial MRI dataset on 20% Lableled data
cd code
nohup python train_cobionet_semi.py --dataset_name LA --labelnum 16 --lamda 0.7 --consistency 1.0 --mu 0.01 --t_m 0.4 --max_iteration 15000 &> la_20_perc.out &
  • To train the model for MSD BraTS MRI dataset on 10% Lableled data
cd code_msd_brats
nohup python train_cobionet_semi.py --dataset_name MSD_BRATS --labelnum 39 --lamda 1.0 --consistency 1.0 --mu 0.01 --t_m 0.25 --max_iteration 10000 &> msd_10_perc.out &
  • To train the model for MSD BraTS MRI dataset on 20% Lableled data
cd code_msd_brats
nohup python train_cobionet_semi.py --dataset_name MSD_BRATS --labelnum 77 --lamda 1.0 --consistency 1.0 --mu 0.01 --t_m 0.25 --max_iteration 10000 &> msd_20_perc.out &

It would take around 5 hours to complete model training for Pancreas and Left Atrium datasets. For MSD BraTS dataset, it will take around 12 hours to complete training. You can try out different hyper-parameter settings and further improve the accuracy.

Hyperparameter Setting and Experimental Results for different data splits.

Hyperparameter Setting

Test Model

  • To test the Co-BioNet ensemble model for Pancreas CT dataset on 10% Lableled data
cd code
python eval_3d_ensemble.py --dataset_name Pancreas_CT --labelnum 6

Acknowledgements

This repository makes liberal use of code from SASSNet, UAMT, DTC and MC-Net

Citing Co-BioNet

If you find this repository useful, please consider giving us a star ⭐ and cite our work:

     @article{peiris2023uncertainty,
        title={Uncertainty-guided dual-views for semi-supervised volumetric medical image segmentation},
        author={Peiris, Himashi and Hayat, Munawar and Chen, Zhaolin and Egan, Gary and Harandi, Mehrtash},
        journal={Nature Machine Intelligence},
        pages={1--15},
        year={2023},
        publisher={Nature Publishing Group UK London
      }
}
      Peiris, Himashi (2023): Project Contributions. figshare. Journal contribution. https://doi.org/10.6084/m9.figshare.22140194.v5