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arxiv: 2605.04201 · v1 · submitted 2026-05-05 · 💻 cs.CV

Topology-Constrained Quantized nnUNet for Efficient and Anatomically Accurate 3D Tooth Segmentation

Paarth Prasad , Ruchika Malhotra This is my paper

Pith reviewed 2026-05-08 17:37 UTC · model grok-4.3

classification 💻 cs.CV
keywords 3D tooth segmentationquantized neural networkstopological constraintsnnUNetCBCT imaginganatomical accuracymedical image segmentationquantization-aware training
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The pith

Integrating a tooth-specific topological loss into quantized nnUNet training reduces anatomical errors in 3D dental segmentation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper shows how to train efficient 8-bit quantized deep learning models for segmenting teeth in cone-beam CT scans without distorting their shapes or counts. It does this by adding a special loss function that penalizes violations of tooth topology during the training process that also accounts for quantization. The result is a model that runs fast on limited hardware but produces segmentations that match real dental anatomy better than standard quantized versions. A sympathetic reader would care because it makes accurate AI tools practical for everyday dental clinics where full-precision models are too slow or power-hungry.

Core claim

The topology-constrained quantized nnUNet framework adds a novel tooth-specific topological loss to the quantization-aware training of an 8-bit nnUNet. This loss uses connected-component analysis, adjacency consistency, and hole detection penalties to preserve tooth count, adjacency relationships, and cavity integrity. Joint optimization with cross-entropy and quantization regularization, enabled by gradient approximations, leads to fewer topological errors and clinically plausible results on dental CBCT scans while retaining integer-only inference efficiency.

What carries the argument

The tooth-specific topological loss combining connected-component analysis, adjacency consistency, and hole detection penalties, integrated into quantization-aware training.

Load-bearing premise

Gradient approximations for the persistent homology terms in the topological loss allow backpropagation through the quantization process without reducing overall segmentation performance.

What would settle it

A direct comparison on dental CBCT scans where the topological error rate of the proposed model matches that of a standard quantized nnUNet would falsify the claim of significant reduction in errors.

Figures

Figures reproduced from arXiv: 2605.04201 by Paarth Prasad, Ruchika Malhotra.

Figure 1
Figure 1. Figure 1: Overall Training Workflow with Topological view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of topological loss in tooth view at source ↗
Figure 4
Figure 4. Figure 4: Relationship between quantization error and view at source ↗
read the original abstract

We propose a topology-constrained quantized nnUNet framework for efficient and anatomically accurate 3D tooth segmentation, addressing the challenges of spatial distortion introduced by quantization in deep learning models. The proposed method integrates a novel tooth-specific topological loss into quantization-aware training, preserving critical anatomical structures such as tooth count, adjacency relationships, and cavity integrity while maintaining computational efficiency. The system employs an 8-bit quantized nnUNet backbone, where weights and activations are dynamically calibrated to minimize precision loss during inference. Furthermore, the topological loss combines connected-component analysis, adjacency consistency, and hole detection penalties, ensuring anatomical fidelity without modifying the underlying network architecture. The joint optimization objective harmonizes cross-entropy loss, quantization regularization, and topological constraints, enabling end-to-end training with gradient approximations for persistent homology terms. Experiments demonstrate that our approach significantly reduces topological errors compared to conventional quantized models, achieving clinically plausible segmentations on dental CBCT scans. The method retains the hardware efficiency of integer-only inference, making it suitable for deployment in resource-constrained clinical environments. This work bridges the gap between computational efficiency and anatomical precision in medical image segmentation, offering a practical solution for real-world dental applications.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes integrating a tooth-specific topological loss (penalizing errors in connected components, adjacency relations, and holes via persistent homology) into the quantization-aware training of an 8-bit nnUNet backbone. The joint objective combines cross-entropy, quantization regularization, and the topological term, with gradient approximations enabling end-to-end optimization. The claimed outcome is reduced topological errors and clinically plausible 3D tooth segmentations on dental CBCT scans while preserving integer-only inference efficiency.

Significance. If the topological constraints demonstrably reduce errors without degrading Dice/IoU or introducing instability under quantization, the work would address a practical gap between model compression and anatomical fidelity in medical segmentation. The approach of adding topology penalties without architecture changes could be relevant for other quantized medical imaging tasks.

major comments (2)
  1. [Abstract] Abstract and Experiments section: the central claim that the method 'significantly reduces topological errors' and yields 'clinically plausible segmentations' is unsupported by any quantitative metrics, baseline comparisons (e.g., standard quantized nnUNet, non-quantized nnUNet), dataset sizes, or statistical tests. Without these, the improvement cannot be assessed.
  2. [Method] Method section on topological loss integration: the assumption that gradient approximations for persistent homology terms remain stable and anatomically meaningful when combined with 8-bit dynamic quantization noise is not validated. No ablation on the approximation (e.g., soft persistence vs. subgradient), no analysis of gradient magnitude under quantization calibration, and no verification that the penalties still enforce tooth count/adjacency/hole constraints post-quantization.
minor comments (2)
  1. The joint loss formulation (CE + quantization reg + topo) should be written explicitly with weighting coefficients and any scheduling details.
  2. Missing discussion of related work on differentiable topological losses (e.g., persistent homology in segmentation) and quantization-aware training for medical volumes.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments highlight important areas where additional quantitative evidence and validation would strengthen the presentation of our topology-constrained quantized nnUNet approach. We address each major comment below and outline the planned revisions.

read point-by-point responses

  1. Referee: [Abstract] Abstract and Experiments section: the central claim that the method 'significantly reduces topological errors' and yields 'clinically plausible segmentations' is unsupported by any quantitative metrics, baseline comparisons (e.g., standard quantized nnUNet, non-quantized nnUNet), dataset sizes, or statistical tests. Without these, the improvement cannot be assessed.

    Authors: We agree that the abstract and experiments section require explicit quantitative metrics, baseline comparisons, dataset details, and statistical tests to properly support the claims of reduced topological errors and clinically plausible segmentations. The current version emphasizes qualitative improvements and efficiency but lacks these elements for rigorous evaluation. In the revised manuscript, we will expand the experiments section to include: (1) quantitative topological error metrics (e.g., connected-component mismatches, adjacency violations, and hole counts) for our method versus standard 8-bit quantized nnUNet and full-precision nnUNet; (2) standard segmentation metrics such as Dice and IoU scores across all comparisons; (3) dataset specifications including the number of dental CBCT scans for training/validation/testing; and (4) statistical tests (e.g., paired t-tests with p-values) to assess significance of improvements. These additions will enable direct assessment of the method's benefits. revision: yes

  2. Referee: [Method] Method section on topological loss integration: the assumption that gradient approximations for persistent homology terms remain stable and anatomically meaningful when combined with 8-bit dynamic quantization noise is not validated. No ablation on the approximation (e.g., soft persistence vs. subgradient), no analysis of gradient magnitude under quantization calibration, and no verification that the penalties still enforce tooth count/adjacency/hole constraints post-quantization.

    Authors: We acknowledge that while the method section outlines the joint optimization with gradient approximations for the persistent homology-based topological penalties, it does not provide explicit validation of their behavior under 8-bit quantization. To address this, the revised manuscript will include: (1) an ablation study evaluating different gradient approximation techniques (e.g., soft persistence versus subgradient approaches) in terms of training stability and final segmentation quality; (2) analysis of gradient magnitudes and their interaction with dynamic quantization calibration; and (3) post-quantization verification by reporting tooth count, adjacency, and hole constraint satisfaction metrics on the integer-only inference outputs. These additions will demonstrate that the topological penalties remain effective and anatomically meaningful despite quantization noise. revision: yes

Circularity Check

0 steps flagged

No significant circularity; claims rest on experimental integration of additive loss

full rationale

The paper presents a methodological framework that adds a tooth-specific topological loss (connected components, adjacency, hole penalties) to standard quantization-aware training of nnUNet, with a joint objective of cross-entropy, quantization regularization, and topological constraints. End-to-end training is enabled via gradient approximations for persistent homology. No equations, derivations, or self-citations are shown that reduce the claimed error reduction to a fitted parameter, self-definition, or imported uniqueness theorem. The central claims are grounded in experimental demonstration on dental CBCT scans rather than any tautological reduction to inputs by construction. This is a standard additive-loss proposal with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the unproven assumption that the added topological penalties can be optimized end-to-end with approximate gradients while preserving quantization benefits; no free parameters or new physical entities are explicitly introduced.

axioms (1)
  • domain assumption Gradient approximations for persistent homology terms enable stable end-to-end training of the joint loss
    Invoked to justify combining topological constraints with quantization regularization without architecture changes.
invented entities (1)
  • tooth-specific topological loss no independent evidence
    purpose: Penalize violations of tooth count, adjacency, and cavity integrity during quantized training
    New composite loss term introduced to enforce anatomical fidelity; no independent evidence provided beyond the claim of reduced errors.

pith-pipeline@v0.9.0 · 5512 in / 1200 out tokens · 39740 ms · 2026-05-08T17:37:38.114430+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

29 extracted references · 29 canonical work pages

  1. [1]

    Segmentation Accuracy: o Dice Similarity Coefficient (DSC): 2|𝑋𝑋∩𝑌𝑌| |𝑋𝑋|+|𝑌𝑌| o I ntersection over Union (IoU): |𝑋𝑋∩𝑌𝑌| |𝑋𝑋∪𝑌𝑌| o Boundary F1 Score (BF1): Harmonic mean of precision and recall for boundary voxels

    work page

  2. [2]

    Topological Fidelity: o Tooth Count Accuracy (TCA): Percentage of scans with correct tooth instances o Adjacency Consistency Score (ACS): 1 − |𝒜𝒜𝑆𝑆𝛥𝛥𝒜𝒜ℳ| |𝒜𝒜ℳ| , where 𝛥𝛥 denotes symmetric difference o C avity Error Rate (CER): 1 𝐾𝐾∑ 𝕀𝕀𝐾𝐾 𝑘𝑘=1�𝛽𝛽1(𝑆𝑆𝑘𝑘) ≠ 𝛽𝛽1(ℳ𝑘𝑘)�

    work page

  3. [3]

    The topological metrics were evaluated on both instance -level and whole -dentition levels to capture local and global anatomical consistency

    Computational Efficiency: o Model Size (MB) o Inference Time per Volume (seconds) o Multiply-Accumulate Operations (MACs) All metrics were computed on the test set using thresholded segmentations at 0.5 probability. The topological metrics were evaluated on both instance -level and whole -dentition levels to capture local and global anatomical consistency...

    work page

  4. [4]

    Full-Precision nnUNet [1]: The original floating- point implementation serving as the accuracy upper bound

    work page

  5. [5]

    Post-Training Quantized nnUNet [2]: Standard 8 - bit quantization applied after training without fine - tuning

    work page

  6. [6]

    QAT-nnUNet[2]: Quantization -aware trained version without topological constraints

    work page

  7. [7]

    For fair comparison, all methods were trained on the same data splits with identical preprocessing

    TopoNet[11]: A topology- preserving segmentation model adapted for dental data. For fair comparison, all methods were trained on the same data splits with identical preprocessing. The QAT variants used the same quantization scheme as our method, while TopoNet employed its original topology loss formulation without quantization. We also included an ablatio...

    work page

  8. [8]

    nnU- net: A self -configuring method for deep learning- based biomedical image segmentation,

    F. Isensee, P. Jaeger, S. Kohl, J. Petersen, et al. , “nnU- net: A self -configuring method for deep learning- based biomedical image segmentation,” Nature Methods, 2021

    work page 2021

  9. [9]

    Quantization and training of neural networks for efficient integer-arithmetic-only inference,

    B. Jacob, S. Kligys, B. Chen, M. Zhu, et al. , “Quantization and training of neural networks for efficient integer-arithmetic-only inference,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018

    work page 2018

  10. [10]

    Three‐ dimensional dental image segmentation and classification using deep learning with tunicate swarm algorithm,

    H. Awari, N. Subramani, A. Janagaraj, et al. , “Three‐ dimensional dental image segmentation and classification using deep learning with tunicate swarm algorithm,” Expert Systems, 2024

    work page 2024

  11. [11]

    A survey on shape - constraint deep learning for medical image segmentation,

    S. Bohlender, I. Oksuz, et al. , “A survey on shape - constraint deep learning for medical image segmentation,” Ieee Reviews in Biomedical Engineering, 2021

    work page 2021

  12. [12]

    ToothSeg: Robust tooth instance segmentation and numbering in CBCT using deep learning and self -correction,

    N. V. Nistelrooij, L. Krämer, S. Kempers, et al. , “ToothSeg: Robust tooth instance segmentation and numbering in CBCT using deep learning and self -correction,” IEEE Journal of Biomedical and Health Informatics, 2026

    work page 2026

  13. [13]

    Tooth segmentation on multimodal images using adapted segment anything model,

    P. Wang, H. Gu, and Y. Sun, “Tooth segmentation on multimodal images using adapted segment anything model,” Scientific Reports, 2025

    work page 2025

  14. [14]

    A geometrically - constrained deep network for CT image segmentation,

    Z. Lambert, C. L. Guyader, et al. , “A geometrically - constrained deep network for CT image segmentation,” in 2021 IEEE 18th international symposium on biomedical imaging (ISBI), 2021

    work page 2021

  15. [15]

    Topological data analysis in medical imaging: Current state of the art,

    Y. Singh, C. Farrelly, Q. Hathaway, T. Leiner, et al. , “Topological data analysis in medical imaging: Current state of the art,” Insights into Imaging, 2023

    work page 2023

  16. [16]

    GFACNet: 3D dental segmentation from intraoral scans integrating geometric features and anatomical constraints,

    G. Zheng, X. Cui, A. Song, and M. Lin, “GFACNet: 3D dental segmentation from intraoral scans integrating geometric features and anatomical constraints,” Electronic Research Archive, 2025

    work page 2025

  17. [17]

    3D dental model segmentation with geometrical boundary preserving,

    S. Xi, Z. Liu, J. Chang, H. Wu, et al. , “3D dental model segmentation with geometrical boundary preserving,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2025

    work page 2025

  18. [18]

    Topology -aware focal loss for 3D image segmentation,

    A. Demir, E. Massaad, and B. Kiziltan, “Topology -aware focal loss for 3D image segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops (CVPRW) 2023, 2023

    work page 2023

  19. [19]

    Combining anatomical constraints and deep learning for 3- d CBCT dental image multi- label segmentation,

    J. Huang, H. Yan, J. Li, H. Stewart, et al. , “Combining anatomical constraints and deep learning for 3- d CBCT dental image multi- label segmentation,” in 2021 IEEE 37th international conference on data engineering, 2021

    work page 2021

  20. [20]

    Latent anomaly detection: Masked VQ -GAN for unsupervised segmentation in medical CBCT,

    P. Wang, “Latent anomaly detection: Masked VQ -GAN for unsupervised segmentation in medical CBCT,” arXiv preprint arXiv:2506.14209, 2025

    work page arXiv 2025

  21. [21]

    Ben-Hamadou, O

    A. Ben -Hamadou, O. Smaoui, A. Rekik, S. Pujades, et al., “3DTeethSeg’22: 3D teeth scan segmentation and labeling challenge,” arXiv preprint arXiv:2305.18277, 2023

    work page arXiv 2023

  22. [22]

    Dints: Differentiable neural network topology search for 3d medical 9 image segmentation,

    Y. He, D. Yang, H. Roth, C. Zhao, et al. , “Dints: Differentiable neural network topology search for 3d medical 9 image segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2021

    work page 2021

  23. [23]

    N., & Pauwels, R

    Pedersen, S., Jain, S., Chavez, M., Ladehoff, V., de Freitas, B. N., & Pauwels, R. (2025). Pano -gan: A deep generative model for panoramic dental radiographs. Journal of Imaging, 11(2), 41

    work page 2025

  24. [24]

    N., & Pauwels, R

    Pedersen, S., Jain, S., Chavez, M., Ladehoff, V., de Freitas, B. N., & Pauwels, R. P. G. (2025). A Deep Generative Model for Panoramic Dental Radiographs. J. Imaging, 11, 41

    work page 2025

  25. [25]

    & Pauwels, R

    Khalil, B., Baraka, M., Haghighat, S., Jain, S., Manila, N., Ramani, R., ... & Pauwels, R. (2025). Synthetic imaging in dentistry: A narrative review of deep learning techniques and applications. Journal of dentistry, 106274

    work page 2025

  26. [26]

    Rubak, J. A. B., Naveed, K., Jain, S., Esterle, L., Iosifidis, A., & Pauwels, R. (2026). Impact of labelling inaccuracy and image noise on tooth segmentation in panoramic radiographs using federated, centralized, and local learning. Dentomaxillofacial Radiology, twag001

    work page 2026

  27. [27]

    N., Basse -OConnor, A., Iosifidis, A., & Pauwels, R

    Jain, S., de Freitas, B. N., Basse -OConnor, A., Iosifidis, A., & Pauwels, R. (2025). PanoDiff -SR: synthesizing dental panoramic radiographs using diffusion and super -resolution. arXiv preprint arXiv:2507.09227

    work page arXiv 2025

  28. [28]

    L., Nosrat, A., & Pauwels, R

    Mohammad‐Rahimi, H., Jain, S., Naveed, K., Hosseinpour, S., Kirkevang, L. L., Nosrat, A., & Pauwels, R. (2026). Generative Artificial Intelligence for Computer Vision in Endodontics: A Review of Current State and Future Potential. International Endodontic Journal

    work page 2026

  29. [29]

    Rubak, J. A. B., Haghighat, S., Jain, S., Aldesoki, M., Chaurasia, A., Ehsani, S. S., ... & Pauwels, R. (2026). Deep Learning-based Assessment of the Relation Between the Third Molar and Mandibular Canal on Panoramic Radiographs using Local, Centralized, and Federated Learning. arXiv preprint arXiv:2603.11850

    work page arXiv 2026