Scaling nnU-Net for CBCT Segmentation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909411444785152 |
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| author | Isensee, Fabian Kirchhoff, Yannick Kraemer, Lars Rokuss, Maximilian Ulrich, Constantin Maier-Hein, Klaus H. |
| author_facet | Isensee, Fabian Kirchhoff, Yannick Kraemer, Lars Rokuss, Maximilian Ulrich, Constantin Maier-Hein, Klaus H. |
| contents | This paper presents our approach to scaling the nnU-Net framework for multi-structure segmentation on Cone Beam Computed Tomography (CBCT) images, specifically in the scope of the ToothFairy2 Challenge. We leveraged the nnU-Net ResEnc L model, introducing key modifications to patch size, network topology, and data augmentation strategies to address the unique challenges of dental CBCT imaging. Our method achieved a mean Dice coefficient of 0.9253 and HD95 of 18.472 on the test set, securing a mean rank of 4.6 and with it the first place in the ToothFairy2 challenge. The source code is publicly available, encouraging further research and development in the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17213 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scaling nnU-Net for CBCT Segmentation Isensee, Fabian Kirchhoff, Yannick Kraemer, Lars Rokuss, Maximilian Ulrich, Constantin Maier-Hein, Klaus H. Computer Vision and Pattern Recognition This paper presents our approach to scaling the nnU-Net framework for multi-structure segmentation on Cone Beam Computed Tomography (CBCT) images, specifically in the scope of the ToothFairy2 Challenge. We leveraged the nnU-Net ResEnc L model, introducing key modifications to patch size, network topology, and data augmentation strategies to address the unique challenges of dental CBCT imaging. Our method achieved a mean Dice coefficient of 0.9253 and HD95 of 18.472 on the test set, securing a mean rank of 4.6 and with it the first place in the ToothFairy2 challenge. The source code is publicly available, encouraging further research and development in the field. |
| title | Scaling nnU-Net for CBCT Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.17213 |