Add Hausdorff loss #6994
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Add Hausdorff loss #6994
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Integrating an existing implementation publicly available on GitHub by Patryk Rygiel into the MONAI framework. Signed-off-by: Imad Toubal <imad.toubal@gmail.com>
Signed-off-by: Imad Toubal <imad.toubal@gmail.com>
Add a `skipUnless` decorator to skip the monai.utils.module.OptionalImportError: `from scipy.ndimage.morphology import distance_transform_edt` Signed-off-by: Imad Toubal <imad.toubal@gmail.com>
Signed-off-by: Imad Toubal <imad.toubal@gmail.com>
Signed-off-by: Imad Toubal <imad.toubal@gmail.com>
Signed-off-by: Imad Toubal <imad.toubal@gmail.com>
Signed-off-by: Imad Toubal <imad.toubal@gmail.com>
KumoLiu
reviewed
Sep 19, 2023
Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Signed-off-by: Wenqi Li <831580+wyli@users.noreply.github.com>
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wyli
approved these changes
Sep 19, 2023
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Integrating an existing implementation publicly available on GitHub by Patryk Rygiel into the MONAI framework.
Fixes #6993.
Fixes #3481
Fixes #1039
Description
Hausdorff distance is widely used in evaluating medical image segmentation methods. Adding an objective/loss function directly to optimize this distance can be instrumental in optimizing this score [1].
An existing implementation is publicly available on GitHub by Patryk Rygiel, although it might take some engineering work to integrate within the MONAI framework.
I was able to train a 3D segmentation model successfully using MONAI's SwinUNETR with this implementation and I would like to contribute the code for this loss so that it's more widely available and easier to integrate with MONAI-based repositories.
References:
[1] Karimi, D., & Salcudean, S. E. (2019). Reducing the Hausdorff distance in medical image segmentation with convolutional neural networks. IEEE Transactions on medical imaging, 39(2), 499-513.
Types of changes
./runtests.sh -f -u --net --coverage../runtests.sh --quick --unittests --disttests.make htmlcommand in thedocs/folder.