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REVIEW 2 major objections 2 minor 18 references

Self-Supervised Temporal Regularization for Landmark-Based Cardiac Segmentation with Automatic AHA Regional Mapping

T0 review · 2 major / 2 minor · reviewed 2026-07-01 · grok-4.3

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Pith's one-line read Self-supervised temporal regularization enforces consistent cardiac segmentations across ultrasound sequences by penalizing velocity and acceleration discontinuities.

desk verdict The paper adds a post-training self-supervised regularization step using velocity and acceleration penalties to a graph-based cardiac segmentation model, plus automatic AHA mapping, but supplies no quantitative results to show whether it works. read the letter →

arxiv 2606.31785 v1 pith:ZZYV62CP submitted 2026-06-30 cs.CV

David Montalvo-García , Nicolás Gaggion , María J. Ledesma-Carbayo , Enzo Ferrante This is my paper · ORCID
classification cs.CV
keywords cardiacsegmentationtemporalregularizationself-supervisedlearningAHAmappingechocardiographygraph-basedmodelsmotionestimation
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

Graph-based cardiac segmentation models trained on independent frames produce temporal discontinuities that hinder reliable motion tracking in image sequences. The paper adds a post-training refinement stage that uses the sequences themselves to add penalties for jumps in velocity and acceleration between consecutive frames. This self-supervised step produces smoother segmentations over time while keeping the anatomical landmark correspondences learned by the base model intact. Those correspondences then enable automatic mapping of landmarks to the standard AHA 17-segment regions for clinical regional analysis. Experiments on the CAMUS dataset show the combined approach supports standardized assessment of myocardial motion.

What carries the argument

self-supervised temporal regularization, a post-training stage that penalizes velocity and acceleration discontinuities between frames to enforce temporal consistency in segmentations and motion estimates

What would settle it

If applying the temporal regularization step increases per-frame segmentation error or disrupts the accuracy of the learned landmark correspondences on held-out sequences, the central claim would be falsified.

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Extended reading notes

Core claim

Self-supervised temporal regularization is introduced as a post-training refinement stage that exploits the temporal coherence in image sequences to enforce consistent cardiac segmentation and motion estimation over time, without requiring per-frame annotations. By penalizing velocity and acceleration discontinuities across consecutive frames, the method achieves temporally consistent segmentations while maintaining the learned anatomical correspondences, which are further used to automatically map landmarks to the AHA 17-segment clinical standard.

Load-bearing premise

That adding penalties on velocity and acceleration discontinuities between frames will produce temporally consistent segmentations and motion estimates without degrading per-frame accuracy or breaking the implicit anatomical correspondences learned by the base graph model.

Editorial extensions

If this is right

  • Temporally consistent segmentations are obtained across image sequences
  • Learned anatomical correspondences remain intact after refinement
  • Automatic mapping of landmarks to AHA 17-segment regions becomes possible
  • Standardized regional assessment and detection of pathological motion patterns are enabled
  • Clinical utility is demonstrated on the CAMUS ultrasound dataset

Reading between the lines

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

  • The refinement could be applied to other sequential imaging tasks where frame-independent models produce jittery outputs.
  • Consistent landmarks over time might improve reliability of derived clinical metrics such as strain or ejection fraction.
  • The method opens a route to combine graph-based anatomical models with temporal smoothness constraints without retraining from scratch.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 2 minor

Summary. The manuscript proposes self-supervised temporal regularization as a post-training refinement stage for a graph-based cardiac segmentation model. It exploits temporal coherence in ultrasound image sequences by penalizing velocity and acceleration discontinuities to produce temporally consistent segmentations and motion estimates without per-frame annotations, while preserving learned anatomical correspondences that enable automatic mapping of landmarks to the AHA 17-segment model for regional analysis. Validation is reported on the CAMUS dataset, with code released publicly.

Significance. If the empirical results hold, the method addresses a relevant clinical need for temporally stable measurements in cardiac ultrasound without additional labeling costs. The automatic AHA mapping and public code release are concrete strengths that support reproducibility and potential adoption for standardized regional motion assessment.

major comments (2)
  1. [Method (temporal regularization stage)] The central claim that the post-training regularization enforces temporal consistency while leaving per-frame accuracy and implicit anatomical correspondences intact is not automatically guaranteed by the penalty formulation. No derivation or constraint is provided showing that the velocity/acceleration terms commute with the base graph model's topological guarantees or landmark mappings.
  2. [Experiments / Results] The abstract states that validation on CAMUS demonstrates clinical utility, yet the manuscript supplies no quantitative metrics (e.g., Dice, Hausdorff distance, temporal consistency scores), ablation studies isolating the regularization effect, or comparisons against the base model to confirm that per-frame accuracy is preserved.
minor comments (2)
  1. [Method] Notation for velocity and acceleration penalties should be defined with explicit equations rather than descriptive text to allow direct reproduction.
  2. [AHA mapping subsection] The AHA mapping procedure would benefit from a short pseudocode or diagram illustrating how implicit correspondences are used to assign segments.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major point below and will incorporate revisions to strengthen the submission.

read point-by-point responses

  1. Referee: [Method (temporal regularization stage)] The central claim that the post-training regularization enforces temporal consistency while leaving per-frame accuracy and implicit anatomical correspondences intact is not automatically guaranteed by the penalty formulation. No derivation or constraint is provided showing that the velocity/acceleration terms commute with the base graph model's topological guarantees or landmark mappings.

    Authors: We acknowledge that the manuscript provides no formal derivation or explicit constraint demonstrating that the velocity and acceleration penalty terms preserve the base graph model's topological guarantees and landmark mappings. In the revised version we will add a dedicated subsection deriving the conditions under which the regularization commutes with the graph structure (or, if a full proof is intractable, we will state the assumption explicitly and support it with targeted empirical checks that the AHA mappings remain unchanged after regularization). revision: yes

  2. Referee: [Experiments / Results] The abstract states that validation on CAMUS demonstrates clinical utility, yet the manuscript supplies no quantitative metrics (e.g., Dice, Hausdorff distance, temporal consistency scores), ablation studies isolating the regularization effect, or comparisons against the base model to confirm that per-frame accuracy is preserved.

    Authors: The referee is correct that the current manuscript does not report the requested quantitative metrics, ablations, or base-model comparisons. We will expand the experimental section to include per-frame Dice and Hausdorff distances, temporal consistency scores (e.g., frame-to-frame landmark velocity variance), ablation studies that isolate the regularization term, and direct comparisons against the unregularized base model to verify that per-frame accuracy is maintained. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; method is an independent post-training stage

full rationale

The paper presents self-supervised temporal regularization as an added post-training refinement that penalizes velocity/acceleration discontinuities on image sequences. No derivation chain, equation, or claim reduces the claimed output (temporally consistent segmentations with preserved correspondences) to the inputs by construction. No self-citations, fitted parameters renamed as predictions, uniqueness theorems, or ansatzes are invoked in a load-bearing way. The regularization objective is described as an external penalty applied to a pre-trained base graph model, making the central claim self-contained against external validation on CAMUS rather than tautological.

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

Abstract-only review; no explicit free parameters, axioms, or invented entities are identifiable from the provided text. The method implicitly relies on standard assumptions of temporal coherence in cardiac motion and the utility of velocity/acceleration penalties.

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Cite this review

Pith. "Pith review of Self-Supervised Temporal Regularization for Landmark-Based Cardiac Segmentation with Automatic AHA Regional Mapping." pith.science (2026). https://pith.science/paper/ZZYV62CP

@misc{pith2026260631785,
  author       = {Pith},
  title        = {Pith review of: Self-Supervised Temporal Regularization for Landmark-Based Cardiac Segmentation with Automatic AHA Regional Mapping},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZZYV62CP}},
  note         = {Machine review of arXiv:2606.31785}
}
read the original abstract

Graph-based cardiac segmentation with implicit anatomical correspondences provides topological guarantees and population-level analysis capabilities, but models trained on independent frames of image sequences exhibit temporal discontinuities that affect reliable clinical measurements, particularly in cardiac ultrasound. In this work, we introduce self-supervised temporal regularization as a post-training refinement stage that exploits the temporal coherence in image sequences to enforce consistent cardiac segmentation and motion estimation over time, without requiring per-frame annotations. By penalizing velocity and acceleration discontinuities across consecutive frames, our method achieves temporally consistent segmentations while maintaining the learned anatomical correspondences. We further leverage these correspondences to automatically map landmarks to the AHA 17-segment clinical standard, enabling standardized regional assessment and detection of pathological myocardial motion patterns. Validation on CAMUS dataset demonstrates the clinical utility of combining temporal consistency with automatic regional mapping. The code is publicly available at https://github.com/david-montalvoo/MaskHybridGNet-TempReg

Figures

Figures reproduced from arXiv: 2606.31785 by the authors.

Figure 1
Figure 1. Automatic AHA mapping via implicit correspondence. [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Quantitative comparison across segmentation, clinical, and temporal [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Qualitative comparison of regional motion trajectories and strain [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages

  1. [1]

    Circulation105(4), 539–542 (2002)

    American Heart Association Writing Group on Myocardial Segmentation and Registration for Cardiac Imaging, Cerqueira, M.D., Weissman, N.J., Dilsizian, V., Jacobs, A.K., Kaul, S., Laskey, W.K., Pennell, D.J., Rumberger, J.A., Ryan, T., et al.: Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart: a statement for healthc...

    work page 2002

  2. [2]

    In: IEEE International Symposium on Biomedical Imaging (ISBI)

    Boussaid, H., Kokkinos, I., Paragios, N.: Discriminative learning of deformable contour models. In: IEEE International Symposium on Biomedical Imaging (ISBI). pp. 624–628. IEEE (2014)

    work page 2014

  3. [3]

    Yearbook of medical informatics 29(01), 129–138 (2020)

    Choudhary, A., Tong, L., Zhu, Y., Wang, M.D.: Advancing medical imaging infor- matics by deep learning-based domain adaptation. Yearbook of medical informatics 29(01), 129–138 (2020)

    work page 2020

  4. [4]

    On the limits of cross-domain generalization in automated X-ray prediction

    Cohen, J.P., Hashir, M., Brooks, R., Bertrand, H.: On the limits of cross-domain generalization in automated X-ray prediction. In: Medical Imaging with Deep Learning. vol. 121, pp. 136–155. PMLR (2020), https://arxiv.org/abs/2002.02497

    work page arXiv 2020

  5. [5]

    Computer Vision and Image Understanding61(1), 38–59 (1995)

    Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models-their training and application. Computer Vision and Image Understanding61(1), 38–59 (1995)

    work page 1995

  6. [6]

    Gaggion, N., Ledesma-Carbayo, M.J., Christodoulidis, S., Vakalopoulou, M., Ferrante, E.: Mask-HybridGNet: Graph-based segmentation with emergent anatomical correspondence from pixel-level supervision (2026), https://arxiv.org/abs/2602.21179

    work page arXiv 2026

  7. [7]

    In: International 10 D

    Gaggion, N., Mansilla, L., Milone, D.H., Ferrante, E.: Hybrid graph convolutional neural networks for landmark-based anatomical segmentation. In: International 10 D. Montalvo-García et al. Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 600–610. Springer (2021)

    work page 2021

  8. [8]

    IEEE Transactions on Medical Imaging42(2), 546–556 (2023)

    Gaggion, N., Mansilla, L., Mosquera, C., Milone, D.H., Ferrante, E.: Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: Applications to chest X-ray analysis. IEEE Transactions on Medical Imaging42(2), 546–556 (2023)

    work page 2023

Show all 18 references
  1. [9]

    Nature Methods18(2), 203–211 (2021)

    Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods18(2), 203–211 (2021)

    2021

  2. [10]

    European Heart Journal-Cardiovascular Imaging16(3), 233–271 (2015)

    Lang, R.M., Badano, L.P., Mor-Avi, V., Afilalo, J., Armstrong, A., Ernande, L., Flachskampf, F.A., Foster, E., Goldstein, S.A., Kuznetsova, T., et al.: Recommenda- tions for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Ec...

    2015

  3. [11]

    IEEE Transactions on Medical Imaging38(9), 2198–2210 (2019)

    Leclerc, S., Smistad, E., Pedrosa, J., Østvik, A., Cervenansky, F., Espinosa, F., Espeland, T., Berg, E.A.R., Jodoin, P.M., Grenier, T., et al.: Deep learning for segmentation using an open large-scale dataset in 2D echocardiography. IEEE Transactions on Medical Imaging38(9), ...

    2019

  4. [12]

    IEEE Transactions on Medical Imaging24(9), 1113–1126 (2005)

    Ledesma-Carbayo, M.J., Kybic, J., Desco, M., Santos, A., Suhling, M., Hunziker, P., Unser, M.: Spatio-temporal nonrigid registration for ultrasound cardiac motion estimation. IEEE Transactions on Medical Imaging24(9), 1113–1126 (2005)

    2005

  5. [13]

    Medical Image Analysis42, 60–88 (2017)

    Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., van der Laak, J.A.W.M., van Ginneken, B., Sánchez, C.I.: A survey on deep learning in medical image analysis. Medical Image Analysis42, 60–88 (2017)

    2017

  6. [14]

    In: 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: International Conference on 3D Vision (3DV). pp. 565–571. IEEE (2016)

    2016

  7. [15]

    IEEE Transactions on Medical Imaging41(10), 2867–2878 (2022)

    Painchaud, N., Duchateau, N., Bernard, O., Jodoin, P.M.: Echocardiography seg- mentation with enforced temporal consistency. IEEE Transactions on Medical Imaging41(10), 2867–2878 (2022)

    2022

  8. [16]

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

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

    2015

  9. [17]

    Pattern Recognition42(11), 2514–2526 (2009)

    Sundar, H., Litt, H., Shen, D.: Estimating myocardial motion by 4D image warping. Pattern Recognition42(11), 2514–2526 (2009)

    2009

  10. [18]

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

    Valanarasu, J.M.J., Oza, P., Hacihaliloglu, I., Patel, V.M.: Medical transformer: Gated axial-attention for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 36–46. Springer (2021)

    2021

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