Scaling nnU-Net for CBCT Segmentation
| Autor: | Isensee, Fabian, Kirchhoff, Yannick, Kraemer, Lars, Rokuss, Maximilian, Ulrich, Constantin, Maier-Hein, Klaus H. |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Fabian Isensee and Yannick Kirchhoff contributed equally |
| Databáze: | arXiv |
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