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\title{Self-Configuring 3D Segmentation of Pediatric Dentition}


%
\author{Enzo Tulissi\inst{1,2} \and Alban Gaydamour\inst{1,2} \and Juan C. Prieto\inst{3} \and Claudia Mattos\inst{4} \and Renata R. Rosa\inst{4} \and Sara Tinawi\inst{1} \and Dylan J. Keener\inst{1} \and Aron Aliaga Del Castillo\inst{1} \and Eduardo Caleme\inst{6} \and Brent Larson\inst{7} \and Antonio C. de Oliveira Ruellas\inst{8} \and Luis E. Arriola-Guillén\inst{9} \and Jonas Bianchi\inst{10} \and Heesoo Oh\inst{10} \and Marcela Lima Gurgel\inst{11} \and Erika Benavides\inst{1} \and Fabiana Soki\inst{1} \and Yalil A. Rodríguez-Cárdenas\inst{12} \and Gustavo A. Ruíz-Mora\inst{12}
\and Bruno M. R. Braga\inst{5} \and Ana B. Teodoro\inst{13}\and Selene Barone\inst{14}\and Martin Styner\inst{3}\and Roberto Bespalez-Neto \inst{15}\and Lucia H. Cevidanes\inst{1}}

\authorrunning{E.Tulissi et al.}

\institute{
University of Michigan, Ann Arbor, MI, USA\and
CPE Lyon, France\and
University of North Carolina, Chapel Hill, NC, USA\and
Universidade Federal Fluminense, Brazil\and
Hospital for Rehabilitation of Craniofacial Anomalies, University of São Paulo, Brazil\and
Universidade Positivo, Curitiba, Paraná, Brazil\and
University of Minnesota, Minneapolis, MN, USA\and
Federal University of Rio de Janeiro, Brazil\and
Universidad Científica del Sur, Lima, Perú\and
University of the Pacific, San Francisco, CA, USA\and
Federal University of Ceara, Fortaleza, Brazil\and
Universidad Nacional de Colombia, Bogotá, Colombia\and
Federal University of Goiás, Goiânia, Brazil\and
Magna Graecia University, Catanzaro, Italy\and
University Anhanguera/Uniderp, Campo Grande, MS, Brazil}
\maketitle

\begin{abstract}
Robust 3D segmentation of primary and permanent teeth in cone-beam CT (CBCT) is critical for pediatric and orthodontic care. We propose a fully automatic deep-learning pipeline built on the self-configuring nnU-Net v2 framework, tailored for high-fidelity dental shape modeling. Our approach learns fine-scale tooth geometries directly from volumetric data, eliminating manual tuning. On a pediatric CBCT cohort (369 training, 93 validation, 55 test scans), our model attains a mean Dice score of 0.87 across 55 dental and supporting anatomical structures. Key components include adaptive preprocessing (isotropic resampling, automatic craniofacial cropping, intensity normalization), on-the-fly 3D augmentations, and lightweight postprocessing to remove spurious segment. The resulting segmentations are consistent and clinically actionable, supporting advanced 3D morphometric analysis and digital treatment planning. By extending state-of-the-art volumetric segmentation to mixed dentition CBCT data, our work facilitates integration of AI-driven geometric learning into routine pediatric dentistry workflows.



\keywords{3D segmentation \and pediatric dentistry  \and Cone-beam CT (CBCT) \and deep learning\and nnU-Net \and  dental morphometry.}
\end{abstract}



\section{Introduction}
Understanding and analyzing the 3D shape of anatomical structures is a cornerstone of medical image computing. In dentistry, tooth morphology includ-
ing crown and root geometries directly impacts diagnosis, treatment planning,
and monitoring.  Recent advances in artificial intelligence, particularly convolutional neural networks (CNNs) have significantly improved the segmentation of anatomical structures in medical images. They now learn shape representations from voxel data, overcoming the limitations of thresholding or region growing caused by partial volume effects and anatomical variability \cite{Lahoud2021,Tarce2024}. Accurate, automatic segmentation of individual teeth in cone-beam CT (CBCT) is especially critical for pediatric mixed dentition, which involves coexisting primary and erupting permanent teeth, unerupted buds, resorbing roots, and impacted teeth all of which introduce significant shape variability. While previous methods have shown strong results for permanent teeth \cite{Lahoud2021}, few have addressed the challenge of comprehensive shape segmentation across all dentition types in pediatric CBCTs. This paper explores a fully automatic segmentation approach using the self-configuring nnU-Net v2 framework \cite{Isensee2021,Isensee2024}. Unlike traditional pipelines that require manual network tuning or rule-based preprocessing, nnUNet v2 adapts its architecture and training plan to the geometry of the input data. This makes it particularly well-suited for tasks involving complex, irregular, and densely packed structures such as mixed dentition. Our study represents the first application of nnUNet v2 to the joint segmentation of both primary and permanent teeth, including all 52 dental and 3 skeletal supporting structures, treated as independent classes in a high-resolution 3D domain. Our contributions are twofold: (1) to design and evaluate a self-configuring deep learning pipeline tailored to pediatric dental anatomy in CBCT; and (2) to demonstrate how this approach enables high-fidelity geometric modeling of the entire dentition, facilitating downstream applications such as orthodontic planning, eruption tracking, and surgical guidance.

\begin{figure}[htbp]
  \centering
  \includegraphics[width=\textwidth]{workflow.png}
  \caption{\textbf{End-to-end automated dental analysis}
  \textbf, deployed as modules of 3D Slicer software \cite{SlicerAutomatedDentalTools} {(A)} \emph{Batch Anonymizer} converts identifiable DICOM image
  stacks into de-identified NIfTI volumes.  
  \textbf{(B)} \emph{Automated Standard Orientation} (ASO) aligns each volume
  to the occlusal and midsagittal planes, ensuring consistent orientation
  across patients.  
  \textbf{(C)} The \emph{BatchDentalSegmentador} module performs fully
  automatic tooth segmentation.  
  \textbf{(D)} Individual tooth meshes undergo statistical shape analysis to
  quantify morphological variability.  
}
  \label{fig:workflow}
\end{figure}


\section{Methods}

% --- place right after \section{Methods} ---
\subsection{Segmentation Method: Overview and Novelty w.r.t.\ nnU\mbox{-}Net~v2}
\label{sec:segmentation-method}
We build on nnU\mbox{-}Net~v2 for 3D dental segmentation, with two focused departures from the vanilla framework. First, we perform mixed-dentition \emph{single-head} labeling across \textbf{55 classes}: in a single forward pass the network predicts all primary and permanent teeth (52 dental labels) together with the mandible, maxilla, and mandibular canal. Prior nnU\mbox{-}Net applications typically address permanent teeth only or a substantially smaller label set.

Second, to preserve laterality, we explicitly disable left--right flips in both data augmentation and test-time augmentation. This deviation from nnU\mbox{-}Net’s default flip policy mitigates contralateral swaps in mixed dentition and stabilizes side-specific tooth labels across upper and lower arches.



\subsection{Dataset and Annotation Strategy}
To enable robust shape modeling of the mixed dentition (Fig.1), we curated a retrospective dataset of 517 pediatric CBCT scans exhibiting both primary and permanent teeth \cite{Cui2022}. Each volume encompasses complete maxillary and mandibular arches and captures a broad spectrum of tooth development stages, including unerupted buds, resorbing roots, and impactions, each presenting unique geometric challenges. On average, each scan includes 6–8 primary teeth and over 28 permanent teeth in varying states of eruption. Initial segmentations were generated using the DentalSegmentator module in 3D Slicer \cite{3DSlicer} and refined manually in ITK-SNAP \cite{ITKSNAP} to assign anatomically precise voxel-wise labels for each discernible tooth. This resulted in a dense multi-class segmentation with 55 total unique anatomical structures, each representing 52 distinct dental shapes, the upper jaw/cranium, lower jaw and the mandibular canal. To make class balance explicit across the 55 classes, Fig.~\ref{fig:label-prevalence} reports, for each label, how many of the 517 scans contain that structure (presence $>$ 0 voxels). Permanent teeth and supporting structures are nearly universal (e.g., upper-right first premolar: 517/517; most premolars/molars $\geq$92\%), whereas age-dependent classes vary widely: third molars appear in $\sim$56--62\% of scans (UR 61.7\%, UL 59.0\%, LL 57.1\%, LR 56.7\%), and primary incisors are scarce (2.7--13.3\%), with primary canines/molars ranging 34--51\%.


\begin{figure}[htbp]
  \centering
  \includegraphics[width=0.9\linewidth]{label_presence_bar.png}% <-- place the file there
  \caption{Per-label prevalence across the cohort (n=517).}
  \label{fig:label-prevalence}
\end{figure}


 Importantly, our labeling captures detailed morphologies, including curved roots, crown morphology, and interproximal spacing, thereby supporting downstream geometric analysis. Unerupted or malformed structures were included when identifiable, ensuring anatomical completeness. The dataset was split into 369 scans for training, 93 for validation, and 55 for testing. This diverse set provides a strong basis for generalizable 3D learning across a spectrum of pediatric dental geometries.






\subsection{
Self-Configuring Segmentation Pipeline
}
We implemented the nnU-Net v2 pipeline \cite{Isensee2021,Isensee2024}, which offers a fully automated segmentation framework that configures all aspects of preprocessing, network architecture, and training strategy based on data-driven heuristics (Fig. 2)\cite{Beser2024}. This design is especially suited for medical shape analysis tasks, where anatomical variability and image heterogeneity demand robust adaptation.

\begin{figure}[H]
  \centering
  \includegraphics[width=0.6\textwidth]{figure_1.png}
  \caption{\label{fig:pipeline}%
Illustration of the nnU-Net v2 segmentation pipeline. Left: coronal slice from the original pediatric CBCT volume. Right: voxel-wise prediction showing all 55 teeth individually.}
\end{figure}

\subsubsection{Preprocessing : }
To prepare the CBCT volumes for geometric learning Fig \ref{fig:crop}, nnU-Net performs: (1) Resolution normalization. All scans are resampled to a consistent isotropic spacing, preserving geometric proportions and enabling scale-invariant feature learning. (2) Spatial cropping. The field-of-view is restricted to the craniofacial region via automatic bounding box detection, focusing the learning process on relevant anatomical context. (3) CT-based intensity normalization. Voxel intensities are clipped and z-normalized to homogenize grayscale representations of bone and soft tissue, preserving contrast critical for shape boundaries. (4) On-the-fly 3D augmentation. During training, randomized transformations such as elastic deformations, rotations, and intensity shifts are applied to simulate anatomical variation and imaging noise without distorting the underlying geometry. These steps produce standardized yet anatomically diverse inputs that allow the network to learn generalizable shape features.



\begin{figure}[H]
\centering
\includegraphics[width=0.6\textwidth]{figure_2.png}
\caption{\label{fig:crop}
\textbf{nnU-Net v2 preprocessing.} (Left) Original axial pediatric CBCT. (Center) After automatic cropping and intensity normalization. (Right) Segmentation of 55 anatomical structure (52 dental and 3 skeletal), plus background (label 0), in the preprocessed space.
}
\end{figure}



\subsubsection{Architecture Configuration :}
nnU-Net v2 automatically selected a full-resolution 3D U-Net with six resolution levels (about 30 M parameters), omitting low-resolution cascades since our volumes fit in GPU memory. Training patches measured 112 x 128 x 128 voxels, covering several teeth while respecting memory limits Fig.\ref{fig:patch}. The encoder applies successive strides to downsample, and a symmetric decoder with skip-connections reconstructs spatial detail. The network concludes with a 1 × 1 × 1 convolution followed by softmax activation and a voxel-wise argmax over 56 classes.  This full-resolution setup proved both accurate and efficient, without needing 2D or multi-resolution variants.





\subsubsection{Automated training schedule : }
We trained using the default nnU-Net v2 optimization, which automatically configures hyperparameters, learning rate scheduling, and optimizer selection. A combined soft Dice and cross-entropy loss handled the severe class imbalance across 56 labels. Each iteration sampled two random \(112\)x\(128\)x\(128\) patches, with on-the-fly augmentations (rotations, elastic deformations, zooms, noise) to improve generalization. We trained for 150 epochs (3000 iterations per epoch) per fold using 5-fold cross-validation and ensembled the softmax outputs, to produce consistent, anatomically robust segmentations.



\begin{figure}[H]
  \centering
  \includegraphics[width=0.4\textwidth]{patch.png}
  \caption{\label{fig:patch}
    \(112\)x\(128^2\) voxel training patches: 
    (A) deciduous incisors and permanent buds; 
    (B) deciduous molars with an erupting first permanent molar. 
    Colored overlays show tooth labels on the CBCT.
  }
\end{figure}



\subsubsection{Postprocessing : }

After inference, connected components are extracted per class and small isolated false positives are removed, retaining the largest anatomically plausible shape per label. This preserves the integrity of each tooth’s morphology and ensures separation of closely spaced structures. This light-weight postprocessing step helps preserve geometric consistency without requiring external priors or templates. Post-segmentation , surface meshes are generated and made available for shape analysis. We enforced shape smoothness via Laplacian regularization.

\section{Results}
\subsection{Quantitative Segmentation Performance}
We tested our nnU-Net v2 on 55 pediatric CBCT scans, reporting Dice (DSC) and Intersection-over-Union (IoU) across all 55 labels. As Figure \ref{fig:dice_iou} and Table \ref{tab:per_class_dice} show, permanent teeth average DSC about 0.90, while smaller primary teeth average about 0.85. All annotated structures were recovered (no missing labels), matching prior CBCT segmentation benchmarks~\cite{Elsonbaty2025}.


\begin{figure}[htbp]
  \centering
  \includegraphics[width=\linewidth]{dice_iou.png}
  \caption{\label{fig:dice_iou}
    Per-class accuracy on the test set.  
    Blue circles = Dice, orange triangles = IoU, for the 55 labels
    ordered from the upper right third molar .
    Most permanent teeth score Dice $\geq$ 0.90, primary teeth around 0.90.  Lower values appear only for the rare third molars.}
\end{figure}

\begin{table}[H]
\centering
\caption{\label{tab:per_class_dice}
Results demonstrate the model’s capacity to robustly segment both primary and permanent teeth across all regions of the dentition. Lower Dice and higher variability are observed for third molars and primary incisors, reflecting anatomical variation and limited sample representation.}
\includegraphics[width=0.7\textwidth, height=0.8\textheight, keepaspectratio]{Dice_heat.png}
\end{table}






To further characterize the segmentation performance at the voxel level, we computed per-class confusion matrices grouped by dentition type. Figure \ref{fig:confmat} shows the normalized confusion matrices separately for permanent and deciduous teeth, divided into upper and lower arches. The matrices highlight that misclassifications, when present, are mostly limited to adjacent teeth or homologous structures, especially among primary teeth with similar shapes.



\begin{figure}[htbp]
  \centering
  \includegraphics[width=0.5\textwidth]{confusion_matrix_4x4.png} % <-- remplace par le nom réel
  \caption{\label{fig:confmat}
  Normalized confusion matrices for upper/lower permanent and upper/lower primary teeth.
  Notably, errors are rare and usually limited to neighboring classes or to the same tooth on the opposite side (i.e., right–left confusion), especially among primary incisors and molars.}
\end{figure}



\subsection{Qualitative visual assessment}

Figure \ref{fig:overlay} shows a representative test-set CBCT with the nnU-Net v2 segmentation overlaid. The automatic masks align closely with true tooth boundaries, isolating each tooth in a distinct color and accurately separating adjacent structures even unerupted buds and resorbing roots. No major label swaps were observed.



\begin{figure}[htbp]
  \centering
  \includegraphics[width=0.7\textwidth]{seg.png}%
  % replace with actual file name
  \caption{\label{fig:overlay}
    Qualitative example of the automatic segmentation.  
    \textbf{Left:} 3-D surface rendering of the maxilla, mandible and teeth
    generated from the predicted labels.  \textbf{Right:} axial, coronal and
    sagittal CBCT slices with the same labels over-laid in semi-transparency.  
}
\end{figure}

Figure~\ref{fig:compare} presents, from left to right, the ground truth, our model's output and the output of DentalSegmentator (nnU\mbox{-}Net v2). Semi-transparent bone surfaces reveal the tooth labels. Compared with \emph{DentalSegmentator}, our method more faithfully reproduces the reality especially at inter-tooth contacts resulting in clearer individualization of teeth and fewer merge errors.


\begin{figure}[h]
  \centering
  \includegraphics[width=0.75\textwidth]{gt_ulab_dentalseg.png}%
  % replace with actual file name
  \caption{\label{fig:compare} Ground Truth vs. Ours vs.  DentalSegmentator
}
\end{figure}


Clinically, our open-source method \cite{BatchDentalSeg} , deployed in the 3D Slicer platform delivers a patient specific digital twin within minutes: each tooth becomes a separate 3D object for measurement or virtual extraction. Segmentation takes ~2 min on GPU (NVIDIA RTX A6000) or <7 min on CPU, cutting manual effort by > 90\% and enabling chair-side deployment \cite{Elsonbaty2025}.

\section{Discussion}
Our results confirm that nnU-Net v2 accurately segments primary and permanent teeth in pediatric CBCT, achieving a mean Dice of 0.87, performance that approaches expert level~\cite{Elsonbaty2025}. We discuss clinical implications, compare with prior dental-segmentation work, and outline limitations and future directions.

Precise 3D tooth segmentation enables advanced geometric analyses in pediatric dentistry. Mixed dentition presents variable anatomy unerupted buds, erupting premolars, resorbing roots while 2D imaging fails to capture these shapes and manual segmentation is slow and inconsistent. We reconstruct the entire dentition volumetrically, each tooth as a separate 3D object. These models support applications like extraction simulation, eruption-path prediction, surgical guidance, and 3D orthodontic planning. Our pipeline is fast, reliable, and maximizes clinical CBCT value. While we emphasize enhancing the utility of already prescribed scans, we also stress the importance of judicious CBCT use in pediatric populations~\cite{Oenning2018}.

We demonstrate how nnU-Net v2, a self-configuring framework, extends DentalSegmentator for pediatric tooth shape modeling~\cite{Lahoud2021,Shaheen2021}, segmenting 55 classes in pediatric CBCT while preserving inter-class 3D geometry. A single nnU-Net generalizes across tooth types and stages without custom tuning, leveraging shape priors during augmentation and outputting semantic labels directly, avoiding heuristic post-processing. Unlike watershed or atlas methods~\cite{Galibourg2017}, our model infers tooth morphology directly while maintaining anatomical coherence.

Where classical methods fail near weak separation or artifacts, our network remains robust through targeted data augmentation. Although Metal artifacts continue to limit segmentation accuracy~\cite{Polizzi2023}, training with bracketed scans may help~\cite{Alqahtani2023}. Reported per-tooth Dice ranges (0.75–0.93)~\cite{Polizzi2023} place our mean of 0.87 in the mid-range: better than early CNNs but below top permanent tooth only pipelines, yet we uniquely handle mixed dentition in one pass. We disabled left–right flips to avoid laterality errors (e.g., mis-labelling contralateral teeth) and achieved 56-class segmentation in a single forward pass streamlining over previous binary or hierarchical approaches while preserving anatomical fidelity.

Limitations include underrepresented anatomical outliers (e.g., syndromic cases) and unmodeled cross-scanner variability. Future work will aim to: (i) expand training to include rare anatomies, (ii) incorporate shape-aware loss functions, (iii) fuse with intraoral scans, (iv) integrate geometric learning tools~\cite{Zhang2023}.



\section{Conclusion}
We have presented a fully automatic segmentation pipeline that leverages the self- configuring nnU-Net v2 framework to model the complete mixed dentition in pediatric CBCT vol- umes. Our approach accurately segments all 55 anatomical structures, including both primary and permanent teeth, while preserving the geometric fidelity of the voxel level. The method achieves a mean Dice score of 0.87 and delivers anatomically coherent segmentations suitable for clinical and computational applications. By capturing the full spatial extent and inter-class relationships of dental structures, our pipeline enables the generation of patient-specific digital twins in which each tooth is represented as a manipulable 3D object. These models support shape-based clinical tasks such as eruption assessment, surgical planning, and orthodontic treatment design. Critically, the workflow runs in near real-time, enabling seamless integration into diagnostic pipelines without manual post processing. 


\begin{credits}
\subsubsection{\ackname} This work was funded by NIH, grant number R01-DE024450.


\subsubsection{\discintname}
The authors have no competing interests to declare that
are relevant to the content of this article.
\end{credits}


\bibliographystyle{splncs04}
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\end{thebibliography}


\end{document}
