Skip to content

Hypernetwork-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels

License

Notifications You must be signed in to change notification settings

sh4174/HypernetEnsemble

Repository files navigation

Hypernet-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels

The implementation of Hypernet-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels (https://arxiv.org/abs/2112.06693).

We aim to improve the segmentation probability estimation of DL networks for medical image segmentation that often suffers from the ambiguity in human annotations and anatomical/pathological structures using the hypernetwork ensemble strategy with the varying Tversky loss.

Human Annotations of Ambiguous Stroke Lesions!

Estimated Segmentation Probability Map!

Segmentation Label Estimation with Different Threshold!

Hypernetwork Architecture

Hypernetwork Architecture!

The overall hypernetwork architecture shown above is implemented in networks/hyper_resunet.py.

Hyperconvolution Blocks!

The hyperconvolution blocks are implemented in blocks/hyper_convolution.py.

Usage

The codes are implemented with the MONAI framework (https://monai.io/), PyTorch (https://pytorch.org/), and PyTorch Lightning (https://www.pytorchlightning.ai/).

Requirements

pip install -r requirements.txt

Example Dependencies

  • monai=0.7.0
  • torch=1.9.1
  • nibabel=3.2.1

Training

The hypernetwork and optimizer is wrapped with PyTorch Lightning (lightning_modules/module_hyper_resunet.py).

Please see train.py for setting up the network parameters and training configurations.

Inference

Please see predict.py for inference.

The network parameters need to be the same with a trained network.

Citation

@misc{hong2021hypernetensemble,
      title={Hypernet-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels}, 
      author={Sungmin Hong and Anna K. Bonkhoff and Andrew Hoopes and Martin Bretzner and Markus D. Schirmer and Anne-Katrin Giese and Adrian V. Dalca and Polina Golland and Natalia S. Rost},
      year={2021},
      eprint={2112.06693},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
} 

Contact

Please contact Sungmin Hong (MGH, HMS shong20@mgh.harvard.edu) if you have questions on the codes or the paper.

Update March 2022: I joined AWS ML Solution Lab. My new email address is hsungmin@amazon.com.

About

Hypernetwork-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages