3D MRI brain tumor segmentation using autoencoder regularization

@inproceedings{Myronenko20183DMB,
  title={3D MRI brain tumor segmentation using autoencoder regularization},
  author={Andriy Myronenko},
  booktitle={BrainLes@MICCAI},
  year={2018}
}
  • A. Myronenko
  • Published in BrainLes@MICCAI 16 September 2018
  • Computer Science
Automated segmentation of brain tumors from 3D magnetic resonance images (MRIs) is necessary for the diagnosis, monitoring, and treatment planning of the disease. [] Key Method Due to a limited training dataset size, a variational auto-encoder branch is added to reconstruct the input image itself in order to regularize the shared decoder and impose additional constraints on its layers. The current approach won 1st place in the BraTS 2018 challenge.
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570 Citations
Highly Influential Citations
71
Background Citations
164
Methods Citations
197
Results Citations
14

570 Citations

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