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.

翻译：本文介绍了我们在锥形束计算机断层扫描（CBCT）图像多结构分割任务中扩展nnU-Net框架的方法，该方法专门针对ToothFairy2挑战赛设计。我们采用nnU-Net ResEnc L模型，通过改进图像块尺寸、网络拓扑结构和数据增强策略，以应对牙科CBCT成像的特殊挑战。我们的方法在测试集上取得了0.9253的平均Dice系数和18.472的HD95值，最终以4.6的平均排名获得ToothFairy2挑战赛冠军。相关源代码已公开，以促进该领域的进一步研究与发展。