Pith. sign in

REVIEW 4 major objections 7 minor 33 references

GEPAR3D: Geometry Prior-Assisted Learning for 3D Tooth Segmentation

T0 review · 4 major / 7 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A single-stage CBCT tooth segmenter that injects a statistical shape model of dentition and models each tooth as a 3D energy basin reaches an average Dice of 95.0%, with the largest gains at root apices.

desk verdict A promising method with a clean ablation, but the external comparison is confounded by train/test overlap and protocol mismatch, so the headline DSC claim needs verification. read the letter →

arxiv 2508.00155 v1 pith:GRXSKBKZ submitted 2025-07-31 eess.IV cs.AIcs.CVcs.LG

Tomasz Szczepański , Szymon P{l}otka , Michal K. Grzeszczyk , Arleta Adamowicz , Piotr Fudalej , Przemys{l}aw Korzeniowski , Tomasz Trzciński , Arkadiusz Sitek This is my paper · ORCID
classification eess.IVcs.AIcs.CVcs.LG
keywords toothsegmentationcone-beamcomputedtomographystatisticalshapemodelgeometricpriordeepwatershedinstancerootresorptionmedicalimage
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

GEPAR3D is a method for segmenting individual teeth in cone-beam CT (CBCT) scans, aimed at the fine root apices that matter for diagnosing orthodontic root resorption. The paper's central claim is that adding two ingredients to a single-stage segmentation network, a statistical shape model of normal dentition used to build a geometry-aware loss and a deep watershed branch that represents each tooth as a continuous 3D energy basin, improves both detection and segmentation. Across external test sets from four centers, GEPAR3D reports an average Dice of 95.0% (+2.8% over the second-best method) and a binary recall of 95.2% (+9.5%), with qualitative gains concentrated at root apices. If this holds, automated CBCT analysis can support reliable baseline segmentation for longitudinal root-resorption assessment.

What carries the argument

The load-bearing machinery is the pairing of a Geometric Wasserstein Dice loss with a deep-watershed instance-regression branch. The GeoWDL is built from an SSM-derived matrix of statistical inter-tooth distances, augmented by quadrant penalty modifiers that make within-arch confusions cheap, across-arch confusions expensive, and tooth-to-background errors most expensive; this matrix replaces the empirical dissimilarity penalties of the Generalized Wasserstein Dice Loss. The instance branch regresses two quantities per voxel: an energy value equal to the Euclidean distance to the nearest tooth boundary, so each tooth is a continuous 3D basin, and a unit vector pointing along the energy descent derived from a 3D Sobel operator. Watershed seeds are extracted from basin halves, the watershed transform separates instances, and per-instance class labels come from majority voting over the semantic segmentation. Together these components let the model resolve the narrow, tapering root apices where intensity contrast is low.

What would settle it

Retrain all five baselines on the exact same 98 scans with the same 32-class re-annotations and the same splits as GEPAR3D, then evaluate on a held-out set whose images are verified by pairwise registration to share no patient with training; if GEPAR3D's +2.8% average Dice margin over the second-best method does not persist, the claim is not supported.

Watch

Extended reading notes

Core claim

On its own terms, the paper establishes that tooth segmentation in CBCT improves when the network is guided by a population-level anatomical prior and when instance separation is posed as an energy-basin regression rather than a separate detection stage. The geometric prior is a Statistical Shape Model of normal dentition from which pairwise inter-tooth distances are extracted and converted into a penalty matrix; the segmentation loss, called GeoWDL, then charges higher cost for confusing morphologically distant or across-arch teeth than for confusing adjacent or symmetric ones. The instance branch regresses a continuous energy map that encodes each voxel's distance to the nearest tooth boundary, plus a per-voxel energy descent direction, and the watershed algorithm on these maps produces separated tooth instances whose labels are assigned by majority voting from the semantic branch. GEPAR3D reports the best average multi-class Dice (95.0%), binary recall (95.2%), and instance detection accuracy (99.2%) against five baselines on external test data from four centers, with the clearest improvements on root apex boundaries.

Load-bearing premise

The reported margins assume the comparison is fair, meaning every baseline is trained under conditions equivalent to GEPAR3D's and the public Cui et al. test set is genuinely external to the training scans, so that the +2.8% Dice difference reflects the method rather than a data or protocol advantage.

Editorial extensions

If this is right

  • Root apex segmentation becomes reliable enough that serial CBCT comparison can detect true root shortening rather than under-segmentation, supporting orthodontic root-resorption assessment.
  • Single-stage instance detection plus multi-class segmentation removes the error accumulation of coarse-to-fine pipelines, raising recall on thin structures without sacrificing precision.
  • The SSM-derived penalty matrix transfers across scanners: a model trained on one public center maintains high Dice on four external centers.
  • The released 32-class re-annotations and code provide a reproducible training and evaluation setup for future tooth-segmentation work.
  • Class-aware penalties from the geometric prior naturally handle third-molar class imbalance better than uniform cross-entropy, since confusions among similar molars are inexpensive.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the SSM prior is derived solely from normal adult dentition, GEPAR3D's margin may narrow on pediatric or severely maloccluded arches; a testable extension is rebuilding the prior from growth-stage or pathological samples.
  • The energy-direction loss is the component most directly aimed at apex boundaries, so an ablation that removes only the direction term while keeping energy regression and GeoWDL would isolate whether the binary-recall gain comes from watershed instance separation or from the geometric prior.
  • A static population-based penalty matrix cannot adapt to patient-specific anomalies; a learned or instance-adaptive geometric prior could push the same machinery further.
  • The reported binary recall gain suggests fewer false negatives in tooth tissue, which is what a baseline root-length measurement would need; quantifying root-length error on the released masks could translate the Dice gain into a clinical number.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 7 minor

Summary. The paper proposes GEPAR3D, a single-step 3D CBCT tooth segmentation method that combines a statistical shape model (SSM) prior encoded in a Wasserstein Dice loss with a 3D deep watershed instance regression branch. The model is trained on 98 publicly available CBCT scans from Cui et al., reannotated into 32 classes, and evaluated on 46 external scans from two in-house and two public centers. The authors report an average DSC of 95.0% (+2.8% over SGANet), a recall of 95.2%, and improved root apex delineation, with ablations attributing the gains to the geometric prior, energy map, and direction losses.

Significance. If the evaluation is validated, GEPAR3D appears to advance the state of the art for root-sensitive tooth segmentation, with a clear architectural contribution and a welcome emphasis on root apices. The planned release of code and reannotated labels is a strength, and the ablation study is logically structured. However, the current manuscript does not establish the external validity or the controlled comparison needed to support the headline claim.

major comments (4)
  1. [Sec. 3 (Datasets and preprocessing), Appendix A, Table 1] The manuscript trains and validates on 98 CBCT scans from Cui et al. [7] and simultaneously lists Cui et al. [7] among the external test datasets, without providing scan IDs, a patient-level split, or a validation partition; this leaves the independence of the Cui et al. test column in Table 1 unverifiable, and if any of the 46 test scans overlap with the 98 training/validation scans, the reported 95.0% average DSC and the +2.8% margin over SGANet reflect in-domain memorization rather than external generalization. Please provide the exact scan IDs and a documented disjoint split, and report the external metrics separately for each center.
  2. [Sec. 3 (Evaluation details), Table 1] The evaluation is not controlled across methods because tooth-specific baselines are 'trained per their original protocols' while GEPAR3D and the general baselines are trained on the reannotated 32-class Cui dataset; a baseline trained on a smaller or differently labeled dataset can underperform for reasons unrelated to GEPAR3D's components, so the comparison does not establish that the proposed method is superior. Please retrain all baselines on identical training folds with identical preprocessing and annotation schema, or restrict the superiority claim to the controlled general baselines and report the training data size and protocol for every method.
  3. [Sec. 3 (Implementation details), Eq. (1)-(2), Table 2] The loss weights Lambda1=10, Lambda2=0.1, Lambda3=1e-6, the background penalty b=2, the quadrant penalty modifiers in Eq. (1), and the watershed threshold beta=0.5 are all hand-set, and no sensitivity analysis is reported; because the ablation gains are small (e.g., rows #6 and #7 differ by 0.05 DSC), the robustness of the central claim to these choices is not established. Please add a sensitivity analysis for the loss weights and penalty values, or justify them with a tuning procedure.
  4. [Sec. 3 (Table 2)] The ablation table does not state the dataset or number of test scans used, nor does it report the statistical test, sample size, or number of runs for the daggered p-values; without this information the small per-component differences cannot be evaluated. Please specify the ablation evaluation set and the statistical protocol.
minor comments (7)
  1. [Abstract and Sec. 1] The phrase '+2.8% over the second-best method' is inconsistent with Table 1, where the second-best overall method (V-Net) is 93.5% DSC; the +2.8% margin is over SGANet, not the second-best overall. Please correct the headline or state that SGANet is the best tooth-specific baseline.
  2. [Table captions] The captions contain 'T able 1' and 'T able 2'; these should be 'Table 1' and 'Table 2'.
  3. [Sec. 3 (Datasets and preprocessing)] The statement 'file IDs in accompanying JSON' does not correspond to any JSON file in the manuscript; please include the file IDs or a link to them.
  4. [Appendix A] The URL 'zenodo.org/records/GEPAR3D' appears to be a placeholder; please provide the actual DOI or permanent link for the released annotations.
  5. [Sec. 2 (Eq. (2) and loss definitions)] The notation p_i, \hat{p}_i, and the exact form of W^M are introduced only in prose; please define all symbols explicitly at first use and clarify that W^M is computed for each voxel i over the class dimension.
  6. [Sec. 2 (Geometric prior)] The expression O = 1/4 (G11 + G21 + G31 + G41) mixes coordinate pairs with scalar weights; please specify that the sum is vector-valued or clarify the indexing of the four central incisors.
  7. [Sec. 3 (Evaluation details)] The paper claims improvements at root apices but evaluates only whole-tooth metrics; please consider reporting a root-apex-specific metric (e.g., DSC restricted to the apical third) to substantiate the qualitative claims.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: GEPAR3D's geometric prior, watershed losses, and evaluation are built on external sources and ground-truth comparisons rather than on self-referential fits.

full rationale

I walked the derivation chain and found no load-bearing step where a prediction reduces to its own input. The geometric prior is an external statistical shape model (Kim et al. [16]) converted into a Wasserstein penalty matrix via declared formulas (Eqs. 1-2); the deep watershed branch is an explicit adaptation of an external method (Bai and Urtasun [2]); and the final loss weights (Lambda_1=10, Lambda_2=0.1, Lambda_3=1e-6), penalty modifiers, and watershed threshold (beta=0.5) are hand-set hyperparameters, not fitted outputs disguised as predictions. The reported DSC/RC/HD are computed against ground-truth labels on test scans, so the performance claim is not identical to any training objective by construction. The only flagged concern is an evaluation-validity issue, not a circular one: Section 3 uses reference [7] for both the 98 training scans and the Cui et al. external test column without documenting a patient-level split; if those test scans come from the same 98, that column would be in-domain and the 'external' label unsupported. This would be data leakage rather than a circular derivation, and the in-house and Tooth Fairy 2 results remain separately reported. The paper's stated limitations (adult-only training, absence of resorbed annotations) are acknowledged and do not introduce circularity.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central comparison rests on several hand-set coefficients and on assumptions about SSM transferability, label-schema consistency, and baseline fairness. The method introduces no new physical entities; the energy basin and geometric prior are computational constructs derived from existing data and standard algorithms.

free parameters (4)
  • Loss weights Lambda1=10, Lambda2=0.1, Lambda3=1e-6 = 10, 0.1, 1e-6
    Empirically set in Section 2 (Implementation details) to balance energy, segmentation, and direction losses; not derived from first principles.
  • Quadrant penalty modifiers = within arch 0.1, between arches 0.2, diagonally 0.3
    Hand-chosen values in Eq. (1) of Section 2 that weight the geometric distance matrix in GeoWDL.
  • Background penalty b=2 = 2
    Hand-set in Appendix D to strongly penalize tooth-to-background misclassification.
  • Watershed seed threshold beta=0.5 = 0.5
    Empirically chosen in Section 2 (Deep Watershed Instance Classification via Majority Voting) to extract seeds at half the energy basin depth.
assumptions (5)
  • domain assumption The SSM-derived inter-tooth distances, averaged over male and female models, provide a valid geometric penalty for CBCT tooth segmentation across four test centers.
    Invoked in Section 2 (Geometric prior) and Appendix C; the SSM is built from a Korean cohort aged 15-30 without third molars, which may not represent the test populations.
  • domain assumption The 32-class Universal Numbering System mapping is consistent among the reannotated training set, the in-house ground truth, and the public test sets.
    Section 3 and Appendix A rely on this mapping; label-schema mismatch would distort Dice and recall scores.
  • domain assumption The 3D deep watershed energy basin plus direction field yields correct instance separation for adjacent teeth and root apices.
    Section 2 adapts the 2D deep watershed [2] to 3D; no theoretical guarantee is given for touching apex geometry.
  • domain assumption Baselines trained under their original protocols are comparable to GEPAR3D trained on the Cui et al. dataset.
    Section 3 states tooth-specific methods are trained per their original protocols; if this implies different training data or hyperparameters, the comparison is confounded.
  • standard math Standard mathematical tools, including the Euclidean distance transform, 3D Sobel-Feldman gradient, and watershed algorithm, behave as expected on CBCT volumes.
    Used in Section 2 for energy map generation, gradient estimation, and instance separation.

how reviews work

0 comments
Cite this review

Pith. "Pith review of GEPAR3D: Geometry Prior-Assisted Learning for 3D Tooth Segmentation." pith.science (2026). https://pith.science/paper/GRXSKBKZ

@misc{pith2026250800155,
  author       = {Pith},
  title        = {Pith review of: GEPAR3D: Geometry Prior-Assisted Learning for 3D Tooth Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GRXSKBKZ}},
  note         = {Machine review of arXiv:2508.00155}
}
read the original abstract

Tooth segmentation in Cone-Beam Computed Tomography (CBCT) remains challenging, especially for fine structures like root apices, which is critical for assessing root resorption in orthodontics. We introduce GEPAR3D, a novel approach that unifies instance detection and multi-class segmentation into a single step tailored to improve root segmentation. Our method integrates a Statistical Shape Model of dentition as a geometric prior, capturing anatomical context and morphological consistency without enforcing restrictive adjacency constraints. We leverage a deep watershed method, modeling each tooth as a continuous 3D energy basin encoding voxel distances to boundaries. This instance-aware representation ensures accurate segmentation of narrow, complex root apices. Trained on publicly available CBCT scans from a single center, our method is evaluated on external test sets from two in-house and two public medical centers. GEPAR3D achieves the highest overall segmentation performance, averaging a Dice Similarity Coefficient (DSC) of 95.0% (+2.8% over the second-best method) and increasing recall to 95.2% (+9.5%) across all test sets. Qualitative analyses demonstrated substantial improvements in root segmentation quality, indicating significant potential for more accurate root resorption assessment and enhanced clinical decision-making in orthodontics. We provide the implementation and dataset at https://github.com/tomek1911/GEPAR3D.

Figures

Figures reproduced from arXiv: 2508.00155 by the authors.

Figure 1
Figure 1. An overview of GEPAR3D, which unifies instance detection and multi-class seg￾mentation for precise tooth root segmentation. (a) Crops the region of interest (ROI) during inference. (b) Simultaneously performs multi-class segmentation and instance regression. (c) Regularizes segmentation loss Lseg with a geometric prior from an SSM of normal dentition [16]. (d) Uses instance regression task LEDT to generate energy ma… view at source ↗
Figure 2
Figure 2. Qualitative comparison of GEPAR3D with the two best-performing methods from quantitative results. Surface Hausdorff Distance heatmaps overlaid on GT labels (green = low, purple = high) highlight apex deviations. GEPAR3D shows superior root sensi￾tivity versus tooth-specific baselines. Missing teeth are shown in gray. nary Recall (RCB). While multi-class metrics assess overall performance, binary metrics focus on too… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

33 extracted references · 33 canonical work pages

  1. [7]

    Nature Communications 13(1), 2096 (2022) 10 T.Szczepański et al

    Cui, Z., Fang, Y., Mei, L., et al.: A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images. Nature Communications 13(1), 2096 (2022) 10 T.Szczepański et al

    work page 2022

  2. [1]

    International Dental Journal61(1), 31–36 (2011)

    Akram, A., Hamid, A.H.Z.A., Razak, J., et al.: MICAP-a novel system for identifi- cation and communication of dental problems. International Dental Journal61(1), 31–36 (2011)

    work page 2011

  3. [2]

    In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    Bai, M., Urtasun, R.: Deep watershed transform for instance segmentation. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 5221–5229 (2017)

    work page 2017

  4. [3]

    IEEE Transactions on Medical Imaging (2024)

    Bolelli, F., Lumetti, L., Vinayahalingam, S., et al.: Segmenting the inferior alveolar canal in CBCT volumes: the toothfairy challenge. IEEE Transactions on Medical Imaging (2024)

    work page 2024

  5. [4]

    IEEE Access 8, 97296–97309 (2020)

    Chen, Y., Du, H., Yun, Z., et al.: Automatic segmentation of individual tooth in dental CBCT images from tooth surface map by a multi-task FCN. IEEE Access 8, 97296–97309 (2020)

    work page 2020

  6. [5]

    Computers in Biology and Medicine 120, 103720 (2020)

    Chung, M., Lee, M., Hong, J., et al.: Pose-aware instance segmentation framework from cone beam CT images for tooth segmentation. Computers in Biology and Medicine 120, 103720 (2020)

    work page 2020

  7. [6]

    In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    Cipriano, M., Allegretti, S., Bolelli, F., et al.: Improving segmentation of the infe- rior alveolar nerve through deep label propagation. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 21137–21146 (2022)

    work page 2022

  8. [8]

    In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    Cui, Z., Li, C., Wang, W.: ToothNet: Automatic tooth instance segmentation and identification from cone beam CT images. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2019)

    work page 2019

Show all 33 references
  1. [9]

    In: Information Processing in Medical Imaging

    Cui, Z., Zhang, B., Lian, C., et al.: Hierarchical morphology-guided tooth instance segmentation from CBCT images. In: Information Processing in Medical Imaging. pp. 150–162. Springer (2021)

    2021

  2. [10]

    In: MICCAI BrainLes Workshop 2017

    Fidon, L., Li, W., Garcia-Peraza-Herrera, L.C., et al.: Generalised wasserstein dice score for imbalanced multi-class segmentation using holistic convolutional net- works. In: MICCAI BrainLes Workshop 2017. pp. 64–76. Springer (2018)

    2018

  3. [11]

    In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 770–778 (2016)

    2016

  4. [12]

    In: International Conference on Medical Image Computing and Computer-Assisted Intervention

    He, Y., Nath, V., Yang, D., et al.: SwinUNETR-V2: Stronger swin transformers with stagewise convolutions for 3d medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 416–426. Springer (2023)

    2023

  5. [13]

    The Journal of the American Dental Association141(1), 32–39 (2010)

    Imbery, T.A., Nehring, J., Janus, C., Moon, P.C.: Accuracy and dimensional stabil- ity of extended-pour and conventional alginate impression materials. The Journal of the American Dental Association141(1), 32–39 (2010)

    2010

  6. [14]

    IEEE Transactions on Pattern Analysis and Machine Intelligence44(10), 6562–6568 (2021)

    Jang, T.J., Kim, K.C., Cho, H.C., et al.: A fully automated method for 3D indi- vidual tooth identification and segmentation in dental CBCT. IEEE Transactions on Pattern Analysis and Machine Intelligence44(10), 6562–6568 (2021)

    2021

  7. [15]

    Dentomaxillofacial Radiology40(1), 24–34 (2011)

    Kapila, S., Conley, R., Harrell Jr, W.: The current status of cone beam computed tomography imaging in orthodontics. Dentomaxillofacial Radiology40(1), 24–34 (2011)

    2011

  8. [16]

    Clinical Oral Investigations pp

    Kim, H.H., Choi, S., Chang, Y.I., et al.: Developing a three-dimensional statisti- cal shape model of normal dentition using an automated algorithm and normal samples. Clinical Oral Investigations pp. 1–14 (2022)

    2022

  9. [17]

    In: International Conference on Medical Image Computing and Computer-Assisted Intervention

    Kim, S., Song, I.S., Baek, S.J.: Automatic segmentation of internal tooth structure from CBCT images using hierarchical deep learning. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 703–713. Springer (2023)

    2023

  10. [18]

    The American Journal of Human Genetics 85(4), 528–535 (2009)

    Kimura, R., Yamaguchi, T., Takeda, M., et al.: A common variation in EDAR is a genetic determinant of shovel-shaped incisors. The American Journal of Human Genetics 85(4), 528–535 (2009)

    2009

  11. [19]

    IEEE Access8, 50507– 50518 (2020)

    Lee, S., Woo, S., Yu, J., Seo, J., Lee, J., Lee, C.: Automated CNN-based tooth segmentation in cone-beam CT for dental implant planning. IEEE Access8, 50507– 50518 (2020)

    2020

  12. [20]

    IEEE Transactions on Medical Imaging41(11), 3116–3127 (2022)

    Li, P., Liu, Y., Cui, Z., et al.: Semantic graph attention with explicit anatomical as- sociation modeling for tooth segmentation from CBCT images. IEEE Transactions on Medical Imaging41(11), 3116–3127 (2022)

    2022

  13. [21]

    Medical Image Analysis 98, 103295 (2024)

    Liu, T., Bai, Q., Torigian, D.A., et al.: VSmTrans: A hybrid paradigm integrating self-attention and convolution for 3D medical image segmentation. Medical Image Analysis 98, 103295 (2024)

    2024

  14. [22]

    IEEE Transactions on Medical Imaging (2024)

    Liu, Y., Zhang, S., Wu, X., et al.: Individual graph representation learning for pediatric tooth segmentation from dental CBCT. IEEE Transactions on Medical Imaging (2024)

    2024

  15. [23]

    In: IEEE International Conference on 3D Vision (3DV)

    Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: Fully convolutional neural networks for volumetric medical image segmentation. In: IEEE International Conference on 3D Vision (3DV). pp. 565–571. IEEE (2016) GEPAR3D: Geometry Prior-Assisted Learning for 3D Tooth Segmentation 11

    2016

  16. [24]

    In: International Conference on Medi- cal Image Computing and Computer-Assisted Intervention

    Płotka, S., Chrabaszcz, M., Biecek, P.: Swin SMT: Global sequential modeling for enhancing 3D medical image segmentation. In: International Conference on Medi- cal Image Computing and Computer-Assisted Intervention. pp. 689–698. Springer (2024)

    2024

  17. [25]

    Clinical Oral Investigations27(7), 3363–3378 (2023)

    Polizzi, A., Quinzi, V., Ronsivalle, V., et al.: Tooth automatic segmentation from CBCT images: a systematic review. Clinical Oral Investigations27(7), 3363–3378 (2023)

    2023

  18. [26]

    In: International Conference on Medical Image Computing and Computer-Assisted Intervention

    Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedi- cal image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 234–241. Springer (2015)

    2015

  19. [27]

    European Journal of Orthodontics41(1), 67–79 (2019)

    Samandara, A., Papageorgiou, S.N., Ioannidou-Marathiotou, I., et al.: Evaluation oforthodonticallyinducedexternalrootresorptionfollowingorthodontictreatment using cone beam computed tomography (CBCT): a systematic review and meta- analysis. European Journal of Orthodontics41(1...

    2019

  20. [28]

    Medical Image Analysis80, 102488 (2022)

    Seidlitz, S., Sellner, J., Odenthal, J., et al.: Robust deep learning-based semantic organ segmentation in hyperspectral images. Medical Image Analysis80, 102488 (2022)

    2022

  21. [29]

    In: International Conference on Medical Image Computing and Computer-Assisted Intervention

    Szczepański, T., Grzeszczyk, M.K., Płotka, S., et al.: Let me DeCode you: Decoder conditioning with tabular data. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 228–238. Springer (2024)

    2024

  22. [30]

    Computers in Biology and Medicine 169, 107839 (2024)

    Tan, M., Cui, Z., Zhong, T., et al.: A progressive framework for tooth and sub- structure segmentation from cone-beam CT images. Computers in Biology and Medicine 169, 107839 (2024)

    2024

  23. [31]

    In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    Tang, Y., Yang, D., Li, W., et al.: Self-supervised pre-training of swin transformers for 3D medical image analysis. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 20730–20740 (2022)

    2022

  24. [32]

    Oral Surgery, Oral Medicine, Oral Pathology58(5), 589–599 (1984)

    Vertucci, F.J.: Root canal anatomy of the human permanent teeth. Oral Surgery, Oral Medicine, Oral Pathology58(5), 589–599 (1984)

    1984

  25. [33]

    PeerJ Computer Science10, e1994 (2024) 12 T.Szczepański et al

    Zheng, Q., Gao, Y., Zhou, M., et al.: Semi or fully automatic tooth segmentation in CBCT images: a review. PeerJ Computer Science10, e1994 (2024) 12 T.Szczepański et al. A GEPAR3D Dataset We train and validate our proposed method using a publicly available dataset comprising 9...

    2024

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.