# Scaling nnU-Net for CBCT Segmentation

arXiv: https://arxiv.org/abs/2411.17213 · HTML: https://mlsanity.com/paper/2411.17213/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 7 references that are in the map.
- First posted on arXiv 26 Nov 2024.

## Builds on (most cited first, 3 of 3)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation](https://mlsanity.com/paper/2404.09556/) (2024)
- [MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation](https://mlsanity.com/paper/2303.14444/) (2023)

## Built on by (most cited first, 4 of 4)

- [U-Mamba2: Scaling State Space Models for Dental Anatomy Segmentation in CBCT](https://mlsanity.com/paper/2509.12069/) (2025)
- [Progressive Growing of Patch Size: Curriculum Learning for Accelerated and Improved Medical Image Segmentation](https://mlsanity.com/paper/2510.23241/) (2025)
- [Shape-aware Sampling Matters in the Modeling of Multi-Class Tubular Structures](https://mlsanity.com/paper/2506.12395/) (2025)
- [Teeth2Point: A Two-Stage Dental CBCT ROI-to-Point Segmentation Framework](https://mlsanity.com/paper/2608.18667/) (2026)

## Abstract (arXiv)

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.