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REVIEW 3 major objections 9 minor 32 references

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble

T0 review · 3 major / 9 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read A 15-fold nnU-Net ensemble segments head and neck tumors on MRI with a blind-test Dice score of 0.81 pre-treatment and 0.70 mid-treatment.

desk verdict A competent challenge report with real blind-test numbers, but the headline claim about 15-fold cross-validation is directly contradicted by the paper's own validation results. read the letter →

arxiv 2412.06610 v1 pith:VU6QHGM4 submitted 2024-12-09 physics.med-ph

Frank N. Mol , Luuk van der Hoek , Baoqiang Ma , Bharath Chowdhary Nagam , Nanna M. Sijtsema , Lisanne V. van Dijk , Kerstin Bunte , Rifka Vlijm
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Peter M.A. van Ooijen
This is my paper · ORCID
classification physics.med-ph
keywords headandneckcancerMRIsegmentationnnU-Netgrosstumorvolumecross-validationensembleadaptiveradiotherapydeeplearningHNTSMRG-24
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

This paper reports an automatic segmentation method for head and neck tumors on T2-weighted MRI, developed for the HNTSMRG-24 challenge. The authors show that a 15-fold cross-validation ensemble of the nnU-Net V2 framework, rather than the standard 5-fold ensemble, reaches a blind-test aggregated Dice coefficient of 0.81 for pre-radiotherapy volumes (0.77 for primary tumors, 0.85 for lymph nodes) and 0.70 for mid-radiotherapy volumes (0.54 and 0.86). For the pre-treatment task, they doubled the training set by adding the corresponding mid-treatment scans; for the mid-treatment task they fed the network a three-channel input of the mid-treatment MRI, the registered pre-treatment MRI, and its label mask. The work matters because reliable automatic contouring on MRI could support adaptive radiotherapy, where target volumes change during treatment.

What carries the argument

The central machinery is the nnU-Net V2 framework in its 3D full-resolution configuration, an automated pipeline that determines preprocessing, architecture, and hyperparameters from the training data. On top of this, the authors replace the default 5-fold cross-validation ensemble with a 15-fold one, so each fold trains on 140 of 150 patients and the final prediction averages 15 models. For the mid-treatment task, the input is a three-channel volume: the mid-RT MRI, the pre-RT MRI registered onto it, and the corresponding label mask of the pre-treatment volumes; the label mask is treated as an image channel, providing prior anatomical context.

What would settle it

Train the same 15-fold nnU-Net ensemble on the mid-RT task using only the single mid-RT MRI channel, keeping everything else identical, and compare the aggregated Dice on the same blind test set; if the score does not drop, the two extra input channels are not responsible for the reported performance.

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Extended reading notes

Core claim

Using the nnU-Net V2 3D full-resolution configuration, the authors trained a 15-fold cross-validation ensemble for both challenge tasks. On the blind test set of 50 patients, the method achieved an aggregated Dice coefficient of 0.81 for Task 1 (pre-RT GTVp and GTVn segmentation) and 0.70 for Task 2 (mid-RT), with lymph-node segmentation consistently strong (0.85 and 0.86) and primary-tumor segmentation the limiting factor, dropping from 0.77 pre-RT to 0.54 mid-RT. The authors attribute the mid-RT drop to tumor shrinkage and reduced contrast, and they conclude that increasing the ensemble from 5 to 15 folds improves robustness and variability.

Load-bearing premise

The assumption that adding the registered pre-RT MRI volume and its label mask as extra input channels improves mid-RT segmentation is the load-bearing premise; the authors chose these channels without an ablation study, so poor registration or mask errors could be hurting the 0.54 GTVp score.

Editorial extensions

If this is right

  • If the method generalizes, it offers a concrete recipe for automatic GTVp and GTVn contouring on T2-weighted MRI for both treatment-planning and mid-treatment adaptive workflows.
  • The 15-fold ensemble, at 15 forward passes per case, still fits within the 20-minute inference limit on a T4 GPU, so the stability gain is practically affordable.
  • The mid-treatment primary-tumor score of 0.54 marks the hardest part of the problem, so future work aimed at low-contrast, shrunken GTVp has an immediate target.
  • The authors' decision to train the pre-RT model on both pre-RT and mid-RT scans shows that combining time-point data can help when the test distribution is the pre-RT domain.

Reading between the lines

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

  • The paper does not report an ablation on the registered pre-RT MRI and mask channels for the mid-RT task, so whether these channels actually help, or whether a simpler single-channel model performs the same, remains open.
  • The reported validation Dice was the same (0.74) for 5-fold and 15-fold ensembles in the mid-RT task, so the paper's claim that more folds improve performance rests on a single blind-test comparison rather than a controlled experiment.
  • A testable extension would be to feed the mid-RT network with the mid-RT mask from the previous planning day (or the registered pre-RT mask as done here) and measure how sensitive the GTVn accuracy is to registration errors.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 9 minor

Summary. This paper reports the RUG_UMCG team's submission to the HNTSMRG-24 challenge, whose two tasks are segmentation of the primary gross tumor volume (GTVp) and metastatic lymph nodes (GTVn) in T2-weighted MRI volumes acquired before (Task 1) and during (Task 2) radiotherapy. The proposed method is the nnU-Net V2 framework in its 3d_fullres configuration, modified from the default 5-fold to a 15-fold cross-validation ensemble; for Task 1, the training set is augmented with mid-RT volumes from the same patients, and for Task 2, a three-channel input is used that stacks the mid-RT volume, the registered pre-RT volume, and the pre-RT label mask. The paper reports validation DSC agg of 0.74 for both the 5-fold and the 15-fold ensemble on Task 2, and blind test results on 50 patients of DSC agg 0.81 (GTVp 0.77, GTVn 0.85) for Task 1 and 0.70 (GTVp 0.54, GTVn 0.86) for Task 2. The conclusion claims that the 15-fold ensemble had a positive effect on segmentation performance in both tasks.

Significance. If the blind test scores are taken at face value, the proposed pipeline is competitive on both tasks, and the per-class breakdown highlights an important open problem: mid-RT GTVp segmentation (DSC agg 0.54) is substantially worse than pre-RT GTVp segmentation (0.77). The paper has several genuine strengths: the aggregated Dice metric is clearly defined (Eq. 1); the null result of the 5-fold versus 15-fold comparison is reported openly rather than hidden; the abandoned approaches (a custom MONAI U-Net and fine-tuned SAM) are described transparently; and the source code is publicly released on Zenodo (§2.5, ref. [21]), which makes the empirical measurements reproducible. These factors support the credibility of the blind test results. At the same time, the stated methodological novelty — the 15-fold ensemble — is not supported by any comparison in the manuscript, and the Task 2 multi-channel input is not ablated, so the contribution as framed is substantially overstated.

major comments (3)
  1. [§2.5 and §4] The conclusion that the 15-fold ensemble 'displayed to have a positive effect on the segmentation performance' is internally contradicted by §2.5, which reports that 'A comparison of training 5-folds and 15-folds resulted in a similar validation DSC agg of 0.74 for both models, both trained for 1250 epochs.' Because the fold count is the paper's only methodological innovation, this null result leaves the central claim of the title, abstract, and conclusion unsupported. The comparison was also performed only for Task 2, so the conclusion's assertion that the benefit holds 'in both pre-RT and mid-RT T2-weighted MRI volumes' has no experimental basis; the related assertion in §3.1 that the 15-fold ensemble provides a larger quality gain than test-time augmentation is likewise unsupported. The authors should either supply evidence of a benefit of 15 folds (for example, per-class and per-fold validation scores for both fold counts, a paired per-patient comparison, or an analysis of prediction variability across folds) or remove the superiority claim and reframe the paper as a challenge-participation report.
  2. [§3.2 and §4] The three-channel input for Task 2 (mid-RT MRI, registered pre-RT MRI, and the pre-RT label mask) was adopted without an ablation study; §3.2 states only that the authors 'opted for including' these channels. Because of this, the contribution of each channel to the final DSC agg is unknown, and the sharp GTVp degradation in Task 2 (DSC agg 0.54 versus 0.77 in Task 1) could plausibly be caused by misregistration of the pre-RT channel, by a learned reliance on the pre-RT mask, or by the intrinsic difficulty of the mid-RT data; the manuscript does not test any of these explanations. The use of the expert pre-RT label mask as an inference-time input is also a notable design choice that deserves explicit discussion, since it means the method depends on the availability of expert annotations at test time. A minimal ablation (for example, training with only the mid-RT volume) and a failure-case analysis for mid-RT GTVp would make the design defensible and would directly address the paper's largest performance drop.
  3. [§3.1] For Task 1, no fold-wise validation DSC agg is reported; the results section provides only a two-patient preliminary score (0.89) and the final 50-patient test score (0.81), so the reader cannot assess model selection for Task 1 or compare it with Task 2, for which validation results are reported. Relatedly, the paper does not specify whether the cross-validation splits are patient-level, which matters for Task 1 because each fold's 280 training samples consist of pre-RT and mid-RT volumes from the same 140 patients; if the split were volume-level, a validation patient's mid-RT volume could leak into the training set. Please report fold-wise validation scores for both tasks and state explicitly how the folds were constructed.
minor comments (9)
  1. [Keywords] The keyword 'HNTSMR24' should read 'HNTSMRG-24' to match the challenge name used in the abstract and in Section 2.1.
  2. [§4, first paragraph] In the sentence 'This framework configures a U-Net architecture [26], , alongside the hyper-parameters and data processing steps,' the doubled comma after the citation should be removed.
  3. [§2.4] In the sentence 'which reduces the the large impact of small GTV volumes,' the doubled 'the' should be removed.
  4. [§2.1] In the sentence reporting GTVn statistics, '14,001 (259 voxels, with 20 patients having no GTVn)' is confusing; the parenthetical appears to be intended as a count of GTVn volumes rather than voxels, and the sentence should be rewritten to match the GTVp reporting format immediately above it.
  5. [§2.5] The selection of 1250 over 1000 epochs is justified by improved DSC agg in 11 of 15 and 12 of 15 folds for GTVp and GTVn, respectively, with an aggregate improvement from 0.73 to 0.74; a two-sided sign test on 11 of 15 is not significant at the 0.05 level (p ≈ 0.12), so this justification should be presented as marginal or supported by an appropriate statistical test.
  6. [Abstract and §3.1] The abstract states that the pre-RT training data (150 pre-RT volumes and masks) was augmented with mid-RT data, whereas Section 3.1 reports per-fold training on 140 pre-RT and 140 mid-RT scans; the abstract should clarify that the stated augmentation is applied per fold of the 15-fold cross-validation.
  7. [§2.2] The phrase 'e.g.99.8% of the voxels is background' should read 'e.g., 99.8% of the voxels are background.'
  8. [§3] The paper does not discuss the gap between the Task 2 validation DSC agg of 0.74 and the blind test DSC agg of 0.70, nor the gap between the two-patient preliminary scores (0.89 for Task 1 and 0.75 for Task 2) and the corresponding final test scores (0.81 and 0.70); brief comments on these gaps would help the reader judge generalization.
  9. [§2.5] For the 5-fold versus 15-fold comparison, only the aggregate validation DSC agg of 0.74 is reported for both configurations; reporting the per-class GTVp and GTVn scores would be more informative, since the two structures show very different behavior on the final test set.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is an empirical challenge report whose results are evaluated on an externally defined blind test set, and its central claims do not reduce to their own inputs.

full rationale

This manuscript contains no derivation chain that could be circular. It reports the application of the nnU-Net V2 framework to an externally organized segmentation challenge, with performance measured by an aggregated Dice Similarity Coefficient defined in a cited external source and computed on a blind test set of 50 patients not used in training. The methodological choices, such as the 15-fold cross-validation ensemble, the inclusion of mid-RT data for Task 1, and the three-channel input for Task 2, are presented as design decisions rather than as predictions derived from first principles. The paper's own comparison showing that a 5-fold and a 15-fold ensemble both reach a validation DSC agg of 0.74 is an internal inconsistency with the conclusion that the 15-fold choice had a positive effect, but this is a correctness or evidentiary concern, not circularity. No equation is defined in terms of the result it is used to predict, no fitted parameter is renamed as a prediction, and no load-bearing claim is justified solely by a self-citation. The absence of an ablation for the added input channels also weakens the support for that design choice, but it does not make the evaluation circular because the final scores are measured on independent blind data. Therefore, the appropriate finding is no significant circularity.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

No new entities are introduced. The paper relies on standard assumptions about label quality and generalizability.

free parameters (3)
  • Number of cross-validation folds = 15
    Chosen to increase ensemble robustness; validation DSC agg (0.74) is identical to the 5-fold baseline.
  • Training epochs for Task 2 = 1250
    Selected because validation DSC agg improved from 0.73 (1000 epochs) to 0.74 (1250 epochs).
  • Training epochs for Task 1 = 1000
    Retained nnU-Net default after exploration showed no improvement.
assumptions (4)
  • domain assumption The merged expert annotations used as ground truth are accurate and consistent.
    All training and evaluation depend on these labels; no label-quality analysis is included.
  • domain assumption The nnU-Net V2 framework's default preprocessing and architecture are appropriate for this task.
    The framework's automatic configuration is adopted without independent validation.
  • domain assumption Validation performance on the training folds generalizes to the blind test set.
    Standard ML assumption; the authors select hyperparameters based on validation and then report test results.
  • standard math The aggregated Dice Similarity Coefficient (Eq. 1) is the intended evaluation metric.
    Defined by the challenge and used consistently.

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Cite this review

Pith. "Pith review of MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble." pith.science (2026). https://pith.science/paper/VU6QHGM4

@misc{pith2026241206610,
  author       = {Pith},
  title        = {Pith review of: MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VU6QHGM4}},
  note         = {Machine review of arXiv:2412.06610}
}
read the original abstract

The superior soft tissue differentiation provided by MRI may enable more accurate tumor segmentation compared to CT and PET, potentially enhancing adaptive radiotherapy treatment planning. The Head and Neck Tumor Segmentation for MR-Guided Applications challenge (HNTSMRG-24) comprises two tasks: segmentation of primary gross tumor volume (GTVp) and metastatic lymph nodes (GTVn) on T2-weighted MRI volumes obtained at (1) pre-radiotherapy (pre-RT) and (2) mid-radiotherapy (mid-RT). The training dataset consists of data from 150 patients, including MRI volumes of pre-RT, mid-RT, and pre-RT registered to the corresponding mid-RT volumes. Each MRI volume is accompanied by a label mask, generated by merging independent annotations from a minimum of three experts. For both tasks, we propose adopting the nnU-Net V2 framework by the use of a 15-fold cross-validation ensemble instead of the standard number of 5 folds for increased robustness and variability. For pre-RT segmentation, we augmented the initial training data (150 pre-RT volumes and masks) with the corresponding mid-RT data. For mid-RT segmentation, we opted for a three-channel input, which, in addition to the mid-RT MRI volume, comprises the registered pre-RT MRI volume and the corresponding mask. The mean of the aggregated Dice Similarity Coefficient for GTVp and GTVn is computed on a blind test set and determines the quality of the proposed methods. These metrics determine the final ranking of methods for both tasks separately. The final blind testing (50 patients) of the methods proposed by our team, RUG_UMCG, resulted in an aggregated Dice Similarity Coefficient of 0.81 (0.77 for GTVp and 0.85 for GTVn) for Task 1 and 0.70 (0.54 for GTVp and 0.86 for GTVn) for Task 2.

Figures

Figures reproduced from arXiv: 2412.06610 by the authors.

Figure 1
Figure 1. Example of pre-RT MRI volume with corresponding annotations. The left panel shows the axial, coronal and sagittal view of the center of volume. The right panel displays the annotated labels for GTVn and GTVp in the axial view. The displayed data is from patient number 30 from the training set. 0, 1, and 2, respectively). These annotations were based on a combination of manual annotations from at least three experts … view at source ↗
Figure 2
Figure 2. Schematic of the U-Net architecture, as conceptually proposed by Ronneberger et al. [26]. In our method, we use the volumetric U-Net that processes 3D volumes. the MRI volumes before training (and for inference). The original voxel size of 0.5 × 0.5 × 2.0 mm3 was kept, i.e. no resizing of the volumes was performed. Since the data consists of large, variably sized volumes, a crop of the volumes was used during traini… view at source ↗
Figure 3
Figure 3. Visual representation of the Dice Similarity Coefficient (DSC) and its aggre￾gated version (DSCagg). Color marks different volumes, namely the expert annotated ground truth volume in orange, the predicted segmentation in purple, and the inter￾section volume indicating the true positive is green. 2.4 Evaluation The evaluation of the segmentation quality of the proposed methods is done by computing the aggregated Dice… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Comparison of 15-fold cross validation of nnU-Net V2 using 1000 and 1250 epochs for mid-RT segmentation (Task 2). The validation DSCagg is displayed for each fold, displaying an increase in performance by training for 1250 epochs above 1000 epochs. The model trained fo…

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Works this paper leans on

32 extracted references · 16 canonical work pages

  1. [21]

    https:// doi.org/10.5281/zenodo.14193311

    Mol, F.: HNTSMRG24_RUG_UMCG version: hntsmrg (Nov 2024). https:// doi.org/10.5281/zenodo.14193311

    work page doi:10.5281/zenodo.14193311 2024

  2. [1]

    In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A

    Andrearczyk, V., Oreiller, V., Abobakr, M., Akhavanallaf, A., Balermpas, P., Boughdad, S., Capriotti, L., Castelli, J., Cheze Le Rest, C., Decazes, P., Cor- reia, R., El-Habashy, D., Elhalawani, H., Fuller, C.D., Jreige, M., Khamis, Y., La Greca, A., Mohamed, A., Naser, M., Prior, J.O., Ruan, S., Tanadini-Lang, S., Tankyevych, O., Salimi, Y., Vallières, M...

    work page 2023

  3. [2]

    In: 3D head and neck tumor segmentation in PET/CT challenge, pp

    Andrearczyk, V., Oreiller, V., Boughdad, S., Rest, C.C.L., Elhalawani, H., Jreige, M., Prior, J.O., Vallières, M., Visvikis, D., Hatt, M., et al.: Overview of the heck- tor challenge at miccai 2021: automatic head and neck tumor segmentation and outcome prediction in pet/ct images. In: 3D head and neck tumor segmentation in PET/CT challenge, pp. 1–37. Spr...

    2021

  4. [3]

    In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

    Andrearczyk, V., Oreiller, V., Jreige, M., Castelli, J., Prior, J.O., Depeursinge, A.: Segmentation and classification of head and neck nodal metastases and primary MRI-based HNC segmentation by 15-fold cross-validation ensemble 11 tumors in pet/ct. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)....

    arXiv 2022

  5. [4]

    In: Head and Neck Tumor Segmentation: First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings 1

    Andrearczyk, V., Oreiller, V., Jreige, M., Vallieres, M., Castelli, J., Elhalawani, H., Boughdad, S., Prior, J.O., Depeursinge, A.: Overview of the hecktor challenge at miccai 2020: automatic head and neck tumor segmentation in pet/ct. In: Head and Neck Tumor Segmentation: First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, Oc...

    work page 2021

  6. [5]

    Bio- engineering 10(2) (2023)

    Avesta, A., Hossain, S., Lin, M., Aboian, M., Krumholz, H.M., Aneja, S.: Com- paring 3d, 2.5d, and 2d approaches to brain image auto-segmentation. Bio- engineering 10(2) (2023). https://doi.org/10.3390/bioengineering10020181, https://www.mdpi.com/2306-5354/10/2/181

    work page doi:10.3390/bioengineering10020181 2023

  7. [6]

    Radiation Oncology15, 1–9 (2020)

    Bielak, L., Wiedenmann, N., Berlin, A., Nicolay, N.H., Gunashekar, D.D., Hägele, L., Lottner, T., Grosu, A.L., Bock, M.: Convolutional neural networks for head and neck tumor segmentation on 7-channel multiparametric mri: a leave-one-out analysis. Radiation Oncology15, 1–9 (2020)

    work page 2020

  8. [7]

    In: 3D Head and Neck Tumor Segmentation in PET/CT Challenge, pp

    Chen, J., Martel, A.L.: Head and neck tumor segmentation with 3d unet and survival prediction with multiple instance neural network. In: 3D Head and Neck Tumor Segmentation in PET/CT Challenge, pp. 221–229. Springer (2022)

    work page 2022

Show all 32 references
  1. [8]

    In: Medical Image ComputingandComputer-AssistedIntervention–MICCAI2016:19thInternational Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19

    Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3d u-net: learning dense volumetric segmentation from sparse annotation. In: Medical Image ComputingandComputer-AssistedIntervention–MICCAI2016:19thInternational Conference, Athens, Greece, October 17-21, 20...

    2016

  2. [9]

    arXiv preprint arXiv:2003.06505 (2020)

    Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Larroy, P., Li, M., Smola, A.: Autogluon-tabular: Robust and accurate automl for structured data. arXiv preprint arXiv:2003.06505 (2020)

    2020 arXiv

  3. [10]

    In: Head and Neck Tumor Segmentation: First Challenge, HECKTOR 2020, Held in Con- junction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings 1

    Ghimire, K., Chen, Q., Feng, X.: Patch-based 3d unet for head and neck tumor segmentation with an ensemble of conventional and dilated convolutions. In: Head and Neck Tumor Segmentation: First Challenge, HECKTOR 2020, Held in Con- junction with MICCAI 2020, Lima, Peru, October...

    2021

  4. [11]

    In: International MICCAI brainlesion workshop

    Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI brainlesion workshop. pp. 272–284. Springer (2021)

    2021

  5. [12]

    Contrast media & molecular imaging2018(1), 8923028 (2018)

    Huang, B., Chen, Z., Wu, P.M., Ye, Y., Feng, S.T., Wong, C.Y.O., Zheng, L., Liu, Y., Wang, T., Li, Q., et al.: Fully automated delineation of gross tumor volume for head and neck cancer on pet-ct using deep learning: A dual-center study. Contrast media & molecular imaging2018(...

    2018

  6. [13]

    Medical physics44(2), 547–557 (2017)

    Ibragimov, B., Xing, L.: Segmentation of organs-at-risks in head and neck ct images using convolutional neural networks. Medical physics44(2), 547–557 (2017)

    2017

  7. [14]

    org/abs/1809.10486

    Isensee,F.,Petersen,J.,Klein,A.,Zimmerer,D.,Jaeger,P.F.,Kohl,S.,Wasserthal, J.,Koehler,G.,Norajitra,T.,Wirkert,S.,Maier-Hein,K.H.:nnu-net:Self-adapting framework for u-net-based medical image segmentation (2018),https://arxiv. org/abs/1809.10486

    2018 arXiv

  8. [15]

    Isensee, F., Wald, T., Ulrich, C., Baumgartner, M., Roy, S., Maier-Hein, K., Jaeger, P.F.: nnu-net revisited: A call for rigorous validation in 3d medical image segmen- tation (2024), https://arxiv.org/abs/2404.09556 12 F. N. Mol et al

    2024 arXiv

  9. [16]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)

    Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., Dollar, P., Girshick, R.: Segment anything. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 4015–4026 (October 2023)

    2023

  10. [17]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision

    Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., et al.: Segment anything. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 4015–4026 (2023)

    2023

  11. [18]

    Nature Communications15(1), 654 (2024)

    Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nature Communications15(1), 654 (2024)

    2024

  12. [19]

    In: 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

    Marstal, K., Berendsen, F., Staring, M., Klein, S.: Simpleelastix: A user-friendly, multi-lingual library for medical image registration. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). pp. 574–582 (2016). https://doi.org/10.1109/CVPRW.2016.78

    2016 doi

  13. [20]

    arXiv preprint arXiv:1908.00841 (2019)

    Moe, Y.M., Groendahl, A.R., Mulstad, M., Tomic, O., Indahl, U., Dale, E., Malinen, E., Futsaether, C.M.: Deep learning for automatic tumour segmenta- tion in pet/ct images of patients with head and neck cancers. arXiv preprint arXiv:1908.00841 (2019)

    2019 arXiv

  14. [22]

    https://doi.org/10.5281/zenodo.12542217

    MONAI Consortium: MONAI: Medical open network for AI: version 1.3.2 (Jun 2024). https://doi.org/10.5281/zenodo.12542217

    2024 doi

  15. [23]

    In: Andrearczyk, V., Oreiller, V., Hatt, M., De- peursinge, A

    Murugesan, G.K., Brunner, E., McCrumb, D., Kumar, J., VanOss, J., Moore, S., Peck, A., Chang, A.: Head and neck primary tumor segmentation using deep neural networks and adaptive ensembling. In: Andrearczyk, V., Oreiller, V., Hatt, M., De- peursinge, A. (eds.) Head and Neck Tu...

    2022

  16. [24]

    Physics and Imaging in Radia- tion Oncology 32, 100655 (2024)

    Reinders, F.C., Savenije, M.H., de Ridder, M., Maspero, M., Doornaert, P.A., Terhaard, C.H., Raaijmakers, C.P., Zakeri, K., Lee, N.Y., Aliotta, E., Rangnekar, A., Veeraraghavan, H., Philippens, M.E.: Automatic segmentation for magnetic resonance imaging guided individual elect...

    2024

  17. [25]

    arXiv preprint arXiv:2402.17454 (2024)

    Ren, J., Rasmussen, M., Nijkamp, J., Eriksen, J.G., Korreman, S.: Segment any- thing model for head and neck tumor segmentation with ct, pet and mri multi- modality images. arXiv preprint arXiv:2402.17454 (2024)

    2024 arXiv

  18. [26]

    CoRRabs/1505.04597 (2015), http://arxiv.org/abs/ 1505.04597

    Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedi- cal image segmentation. CoRRabs/1505.04597 (2015), http://arxiv.org/abs/ 1505.04597

    2015 arXiv

  19. [27]

    CA: a cancer journal for clinicians 71(3), 209–249 (2021)

    Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A., Bray, F.: Global cancer statistics 2020: Globocan estimates of incidence and mor- tality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians 71(3), 209–249 (2021)

    2021

  20. [28]

    arXiv preprint arXiv:2404.16233 (2024)

    Tang, Z., Fang, H., Zhou, S., Yang, T., Zhong, Z., Hu, T., Kirchhoff, K., Karypis, G.: Autogluon-multimodal (automm): Supercharging multimodal automl with foundation models. arXiv preprint arXiv:2404.16233 (2024)

    2024 arXiv

  21. [29]

    Radiotherapy MRI-based HNC segmentation by 15-fold cross-validation ensemble 13 and Oncology 112(3), 317–320 (Sep 2014).https://doi.org/10.1016/j.radonc

    Valentini, V., Boldrini, L., Damiani, A., Muren, L.P.: Recommendations on how to establish evidence from auto-segmentation software in radiotherapy. Radiotherapy MRI-based HNC segmentation by 15-fold cross-validation ensemble 13 and Oncology 112(3), 317–320 (Sep 2014).https://...

    2014 doi

  22. [30]

    Clinical and Translational Radi- ationOncology 32,6–14(2022)

    Wahid, K.A., Ahmed, S., He, R., van Dijk, L.V., Teuwen, J., McDonald, B.A., Salama, V., Mohamed, A.S., Salzillo, T., Dede, C., Taku, N., Lai, S.Y., Fuller, C.D., Naser, M.A.: Evaluation of deep learning-based multiparametric mri oropha- ryngeal primary tumor auto-segmentation ...

    2022 doi

  23. [31]

    In: 3D Head and Neck Tumor Segmentation in PET/CT Challenge, pp

    Xie, J., Peng, Y.: The head and neck tumor segmentation based on 3d u-net. In: 3D Head and Neck Tumor Segmentation in PET/CT Challenge, pp. 92–98. Springer (2021)

    2021

  24. [32]

    In: Head and Neck Tumor Segmentation: First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings 1

    Xie,J.,Peng,Y.:Theheadandnecktumorsegmentationusingnnu-netwithspatial and channel ‘squeeze & excitation’blocks. In: Head and Neck Tumor Segmentation: First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings 1. pp. 28–36. Spr...

    2021

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.