DentalSegmentator-KonfAI

KonfAI adaptation of DentalSegmentator β€” dento-maxillo-facial CT / CBCT segmentation, built with KonfAI.

🧩 Model

Task Modality Labels Ensemble
DentalSegmentator CT / CBCT 5 1

Labels: 1 Maxilla & Upper Skull Β· 2 Mandible Β· 3 Upper Teeth Β· 4 Lower Teeth Β· 5 Mandibular canal.

nnU-Net v2 3d_fullres PlainConvUNet (Dataset111_453CT, fold 0), converted weight-exact by build.py Β· patch [160, 128, 112] Β· resampled to 0.45 Γ— 0.45 Γ— 0.31 mm.

πŸš€ Usage

  • Generic runner: konfai-apps infer VBoussot/DentalSegmentator-KonfAI:DentalSegmentator -i input_cbct.nii.gz -o output/
  • Interactive: SlicerKonfAI
  • TTA / uncertainty: up to 7 random mirror copies (--tta 7; the model was trained with mirroring on all axes), -uncertainty for the per-voxel label variance.

⚑ Performance

Slicer's PostDentalSurgery CBCT (360 Γ— 360 Γ— 330, 0.5 mm), same weights, same PyTorch build (2.12.1, cu13.0), single NVIDIA RTX PRO 5000 (24 GB), nnU-Net 2.x without TTA (2026-09-30).

Tool Time Peak RAM
KonfAI 15.9 s 2.3 GB
Original (nnU-Net) 60.7 s 6.4 GB

Dice against the original's output: 0.976 / 0.985 / 0.984 / 0.982 / 0.950 (labels 1–5), the same spread as the original with vs. without its mirroring TTA (0.988 / 0.985 / 0.988 / 0.982 / 0.934).

πŸ“š Citation

Dot G, et al. DentalSegmentator: robust open source deep learning-based CT and CBCT image segmentation. Journal of Dentistry (2024) doi:10.1016/j.jdent.2024.105130

Isensee F, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18(2):203-211. doi:10.1038/s41592-020-01008-z

βš–οΈ License

The weights are DentalSegmentator's nnU-Net parameters by Dot et al., taken from the v1.0.0-alpha release and converted to KonfAI's format without retraining. The authors publish this model on Zenodo under CC BY 4.0, so the converted weights are shared here under the same CC BY 4.0 license: cite the papers above when you use them. The configuration files and build.py are Apache-2.0.

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