TEDS-Net: enforcing diffeomorphisms in spatial transformers to guarantee topology preservation in segmentations
Accurate topology is key when performing meaningful anatomical segmentations, however, it is often overlooked in traditional deep learning methods. In this work we propose TEDS-Net: a novel segmentation method that guarantees accurate topology. Our method is built upon a continuous diffeomorphic fra...
| 主要な著者: | , , , |
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| フォーマット: | Conference item |
| 言語: | English |
| 出版事項: |
Springer
2021
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