改进nnU-Net在牙科CBCT图像多结构分割上的表现，获挑战赛第一名。
Scaling nnU-Net for CBCT Segmentation
- 调整块大小、网络结构和数据增强策略适配牙科CBCT特点。
- 测试集平均Dice达0.9253，HD95为18.472，排名均分4.6。
- 代码开源，适合医学图像分割研究者参考复现。
本文提出一种将nnU-Net框架扩展用于锥形束计算机断层扫描（CBCT）图像多结构分割的方法，聚焦于ToothFairy2挑战赛。我们采用nnU-Net ResEnc L模型，通过调整块大小、网络拓扑结构及数据增强策略，应对牙科CBCT影像的独特挑战。方法在测试集上取得平均Dice系数0.9253和HD95值18.472，平均排名4.6，位列第一。源代码已公开，鼓励该领域进一步研究与开发。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。