REVIEW 2 major objections 2 minor 8 references
Temporal Training Strategies for Left Atrium and Left Atrial Appendage Segmentation in Dynamic Contrast 4DCT
T0 review · 2 major / 2 minor · reviewed 2026-07-01 · grok-4.3
Pith's one-line read Training on full 27-frame sequences outperforms reduced sets for early low-contrast phases in LA and LAA segmentation from dynamic 4DCT.
desk verdict Full-frame training edges out subsets in early low-contrast phases for LA/LAA segmentation but a physiological subset matches later, though single-annotation label noise via registration could be inflating that gap. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
Temporal training-set design comparing minimal two-frame, physiologically selected, and full 27-frame datasets for nnUNet segmentation, together with foreground-based normalization derived from the complete sequence.
What would settle it
Independent per-frame ground-truth annotations on a held-out test sequence, followed by retraining the three strategies and direct comparison of Dice or surface-distance metrics in the early low-contrast phases.
Extended reading notes
Core claim
The authors establish that training nnUNet models on the full 27-frame dynamic sequences yields the best segmentation performance in early low-contrast phases of the left atrium and left atrial appendage. A physiologically selected subset of frames achieves comparable performance from the filling phase onward. Applying normalization parameters derived from the full dataset improves performance of the reduced datasets in low-contrast frames but does not fully close the gap to the full-set model.
Load-bearing premise
A single annotation per registered sequence provides sufficient ground truth across all temporal frames without introducing label noise that systematically biases the comparison between training-set designs.
Editorial extensions
If this is right
- Full-frame training is required to achieve optimal robustness during early low-contrast phases.
- Physiologically selected frames provide a practical trade-off for segmentation once contrast filling has started.
- Normalization parameters from the full dataset partially compensate for smaller training sets but do not eliminate the advantage of temporal diversity.
- Downstream time-resolved contrast analysis benefits when training data include the full range of temporal contrast states.
Reading between the lines
- Reduced frame sets could lower annotation cost for clinical pipelines focused on post-filling phases without major accuracy loss.
- The same temporal-design logic may apply to other dynamic contrast modalities where label propagation from one frame is common.
- Explicit per-frame re-annotation experiments would quantify how much label noise currently limits the reduced-set approaches.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper evaluates temporal training-set designs for nnUNet segmentation of the left atrium (LA) and left atrial appendage (LAA) in dynamic contrast 4DCT. It compares a minimal two-frame set (standard practice), a physiologically selected subset, and the full 27-frame sequence, plus the effect of foreground-based normalization derived from the full dataset. The central empirical claim is that full-frame training yields the best performance in early low-contrast phases, the physiological subset matches from the filling phase onward, and normalization improves reduced datasets in low-contrast frames without fully closing the gap. The work highlights a trade-off between temporal diversity and label noise arising from single per-sequence annotations propagated by registration.
Significance. If the ordering holds under proper controls, the results offer actionable guidance for efficient training of dynamic cardiac CT segmentations relevant to blood-stasis assessment in atrial fibrillation. The purely empirical, held-out comparison of training regimes is a strength; reproducible code or public data splits would further strengthen it.
major comments (2)
- [Methods] Methods (annotation and registration subsection): the central performance ordering rests on a single manual annotation per registered sequence serving as ground truth for every temporal frame and every training-set variant. No quantitative validation of registration fidelity (e.g., landmark error, inter-frame Dice on propagated labels, or contrast-phase-specific boundary consistency) is reported. Systematic label noise that varies with contrast level could therefore artifactually inflate the reported advantage of the full 27-frame set in early phases.
- [Results] Results (performance tables/figures): the abstract and summary state clear ordering claims, yet the provided text supplies neither patient counts, cross-validation scheme, nor statistical tests (paired t-tests or Wilcoxon with correction) on the reported metrics. Without these, it is impossible to judge whether the observed gaps exceed inter-patient variability or are driven by a few outlier cases.
minor comments (2)
- [Abstract] Abstract: states performance ordering without any numerical values, patient numbers, or error bars; this should be supplemented with at least the key Dice or surface-distance figures.
- [Methods] Notation: “foreground-based normalization” is introduced without an explicit equation or reference to the exact intensity statistics used; add a short methods paragraph or equation.
Simulated Author's Rebuttal
We thank the referee for the detailed and constructive review. Below we respond point-by-point to the major comments, indicating where revisions will be made.
read point-by-point responses
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Referee: [Methods] Methods (annotation and registration subsection): the central performance ordering rests on a single manual annotation per registered sequence serving as ground truth for every temporal frame and every training-set variant. No quantitative validation of registration fidelity (e.g., landmark error, inter-frame Dice on propagated labels, or contrast-phase-specific boundary consistency) is reported. Systematic label noise that varies with contrast level could therefore artifactually inflate the reported advantage of the full 27-frame set in early phases.
Authors: We agree that explicit quantitative registration validation is not reported. However, because the identical propagated labels serve as ground truth for every training-set variant, any contrast-dependent label noise affects all models equally; relative differences therefore reflect temporal diversity rather than differential noise. We will add a limitations paragraph discussing registration quality and its potential impact. revision: partial
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Referee: [Results] Results (performance tables/figures): the abstract and summary state clear ordering claims, yet the provided text supplies neither patient counts, cross-validation scheme, nor statistical tests (paired t-tests or Wilcoxon with correction) on the reported metrics. Without these, it is impossible to judge whether the observed gaps exceed inter-patient variability or are driven by a few outlier cases.
Authors: The revised manuscript will explicitly report the patient count, the cross-validation scheme, and the results of paired statistical tests (Wilcoxon signed-rank with Bonferroni correction) comparing the training regimes. These additions will allow readers to evaluate whether the reported ordering exceeds inter-patient variability. revision: yes
Circularity Check
No circularity: purely empirical comparison of training regimes
full rationale
The manuscript reports an empirical ablation of temporal training-set designs (2-frame, physiological subset, full 27-frame) for nnUNet segmentation of LA/LAA in dynamic 4DCT, with performance measured on held-out frames. No equations, parameter-fitting steps, or derivations are present that could reduce any reported ordering or performance gap to a tautology by construction. The single-annotation-per-sequence setup is an explicit methodological choice whose label-noise implications are discussed as a limitation rather than smuggled into a self-referential result. All claims rest on direct metric comparison against external test data and are therefore self-contained.
Assumptions & free parameters
assumptions (1)
- domain assumption nnUNet architecture and default training procedure are appropriate and stable for dynamic contrast 4DCT segmentation
Cite this review
Pith. "Pith review of Temporal Training Strategies for Left Atrium and Left Atrial Appendage Segmentation in Dynamic Contrast 4DCT." pith.science (2026). https://pith.science/paper/MLO2JHTH
@misc{pith2026260631444,
author = {Pith},
title = {Pith review of: Temporal Training Strategies for Left Atrium and Left Atrial Appendage Segmentation in Dynamic Contrast 4DCT},
year = {2026},
howpublished = {\url{https://pith.science/paper/MLO2JHTH}},
note = {Machine review of arXiv:2606.31444}
}
read the original abstract
Dynamic contrast-enhanced cardiac CT enables time-resolved analysis of contrast filling and washout in the left atrium (LA) and left atrial appendage (LAA), with potential applications for assessing blood stasis in atrial fibrillation (AF). Accurate segmentation across all frames is required for such analysis but is challenging due to large temporal contrast variations and the use of a single annotation per registered sequence. This creates a trade-off between training for robustness and limiting label noise. In this study, we investigate how temporal training-set design affects nnUNet-based segmentation of the LA and LAA in dynamic 4DCT. We compare training using a minimal two-frame dataset reflecting standard clinical practice, a physiologically selected subset of frames, and the full 27-frame sequence. We further evaluate the impact of foreground-based normalization. Training with all frames yielded the best performance in early low-contrast phases. However, the physiologically selected subset achieved comparable performance from the filling phase onward. Applying normalization parameters derived from the full dataset improved performance of reduced datasets in low-contrast frames, but did not fully close the gap. These findings highlight the importance of temporal diversity in training data for robust segmentation in dynamic CT, while indicating that carefully selected frame subsets may provide an effective trade-off between performance and efficiency for downstream applications.
Figures
Reference graph
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Reviewed July 1, 2026 · model on record in the stance chip above.
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