Scaling nnU-Net for CBCT Segmentation

• URL: http://arxiv.org/abs/2411.17213v1
• Date: Tue, 26 Nov 2024 08:29:24 GMT
• Title: Scaling nnU-Net for CBCT Segmentation
• Authors: Fabian Isensee, Yannick Kirchhoff, Lars Kraemer, Maximilian Rokuss, Constantin Ulrich, Klaus H. Maier-Hein,
• Abstract summary: This paper presents our approach to scaling the nnU-Net framework for multi-structure segmentation on Cone Beam Computed Tomography (CBCT) images. We leveraged the nnU-Net ResEnc L model, introducing key modifications to patch size, network topology, and data augmentation strategies to address the 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.
• Score: 0.9854844969061186
• License:
• Abstract: 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.

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