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CineMyoPS: Segmenting Myocardial Pathologies from Cine Cardiac MR

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read CineMyoPS claims that a single contrast-free cine cardiac MR sequence is enough to jointly segment myocardial scar and edema.

desk verdict First joint scar/edema segmentation from cine CMR with fair baselines, but the evaluated model doesn't match the described one and the label registration is unvalidated. read the letter →

arxiv 2507.02289 v1 pith:YTJSXPSV submitted 2025-07-03 eess.IV cs.CV

Wangbin Ding , Lei Li , Junyi Qiu , Bogen Lin , Mingjing Yang , Liqin Huang , Lianming Wu , Sihan Wang
show 1 more author
Xiahai Zhuang
This is my paper · ORCID
classification eess.IVcs.CV
keywords myocardialinfarctioncinecardiacMRIscarsegmentationedemamotionestimationcontrast-freeCMRdeepneuralnetworktime-seriesaggregation
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

CineMyoPS sets out to prove that myocardial scar and edema — two markers of heart attack damage normally seen only on contrast-enhanced LGE and T2-weighted MRI — can be segmented from cine cardiac MR alone, a rapid, contrast-free sequence. The paper argues that motion, anatomy, and time-series texture cues in a cine loop carry enough information to replace multi-sequence imaging for area-at-risk assessment. To show this, it trains an end-to-end network that estimates heart motion between frames, segments myocardial anatomy, and aggregates features across the cardiac cycle, with a consistency loss tying the two learned tasks together. On a multi-center test set, the method reaches Dice scores of 0.53 for scar and 0.57 for edema, higher than four baselines trained under the same protocol. If these results hold, contrast agents and long scanning sessions could be avoided in routine infarct assessment.

What carries the argument

The load-bearing mechanism is a three-module network: a motion estimation module built as a U-shaped registration subnetwork that predicts a dense displacement field from each frame to the end-diastolic reference, an anatomy segmentation module, and a MyoPS module that concatenates motion, anatomy, and texture features in the reference image space. Two devices carry the argument: a consistency loss, defined as cosine distance between the anatomy prediction warped by the motion field and the reference anatomy label, which co-trains motion and anatomy; and a time-series aggregation strategy that sums per-frame pathology predictions across the cardiac cycle and passes them through softmax(conv(·)). This lets the network exploit the interdependence of motion and structure while integrating information from multiple cardiac phases.

What would settle it

An external test set whose scar and edema labels come from histology, or from an independent registration pipeline rather than the MvMM tool, would settle the claim: if CineMyoPS's Dice score against those labels falls to the level of the baselines, the reported advantage is an artifact of the fused gold standard.

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Extended reading notes

Core claim

The paper's central claim is that CineMyoPS is the first fully automatic network to segment both scars and edema from cine CMR images in an end-to-end fashion. It finds that explicit motion features — dense displacement fields produced by a registration subnetwork — are the single most informative cue for pathology segmentation, outperforming texture and anatomy features, and that combining motion with anatomy features gives the best overall results. The authors further report that the proposed consistency loss and time-series aggregation each produce statistically significant gains over ablated variants, and that on the held-out test center CineMyoPS outperforms nnU-Net, OFSeg, ConvLSTM, and 2D+1D U-Net under the same training protocol, with test Dice scores of 0.53 for scar and 0.57 for edema.

Load-bearing premise

The pathology labels used as ground truth are accurate enough to train and test on, even though they were created by registering rater annotations from LGE and T2w images onto the cine frames.

Editorial extensions

If this is right

  • If correct, a full area-at-risk assessment for myocardial infarction could be obtained from a single rapid, contrast-free cine acquisition, removing gadolinium injection and roughly half the scan time associated with LGE.
  • Motion and anatomy features are the effective carriers of scar and edema information; texture alone adds noise once both are present, so future cine-based methods should prioritize deformation and structural cues.
  • Adding temporal frames helps up to a point: using four of six frames in the cardiac cycle reaches a plateau, giving a computational budget for future models.
  • The consistency loss means only the end-diastolic frame needs manual anatomy labeling during training, reducing annotation burden for cine-sequence segmentation.
  • Transmurality estimates correlate well with manual delineation in viable and intermediate regions but fail in fully nonviable myocardium, so the method supports screening but not yet full infarct-extent quantification.

Reading between the lines

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

  • Editorial inference: the reported Dice ceiling sits near the inter-observer agreement (0.69 for scar, 0.73 for edema), so the practical limit of cine-only pathology segmentation may be partly set by label ambiguity rather than by the network; better label fusion could lift apparent performance without changing the model.
  • Editorial inference: since the model was trained on pre- and post-contrast cine but tested only on pre-contrast cine, the texture-feature drop may reflect a domain gap; training solely on pre-contrast cine, or explicit domain adaptation, is a natural next experiment.
  • Editorial inference: the apical-slice failures and the failure in nonviable-region transmurality suggest the method's errors concentrate in thin-wall, high-curvature regions; a slice-aware or shape-constrained extension is a testable route to improvement.
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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 / 5 minor

Summary. The manuscript proposes CineMyoPS, an end-to-end network that segments myocardial scar and edema from cine CMR only. It combines a motion estimation module, an anatomy segmentation module, and a pathology segmentation module, with a consistency loss to couple motion and anatomy features and a time-series aggregation over a subset of the cardiac cycle. Experiments on 145 patients from three centers (training and validation on Center-M/Center-Z, held-out test on Center-R) report Dice scores of 0.53 for scar and 0.57 for edema, together with comparisons against nnU-Net, OFSeg, ConvLSTM, and 2D+1D U-Net trained under the same protocol. The paper also includes ablation studies on frame proportion, feature combinations, the consistency loss, and a transmurality correlation analysis.

Significance. If the results hold, the paper provides a useful demonstration that cine-only CMR can support joint scar and edema segmentation in a multi-center setting, and the controlled same-protocol comparison with four baselines is a strength. The authors are also transparent about limitations, including the weak correlation in nonviable transmurality (R=0.22, p=0.17) and difficulties in apical slices. However, the significance is currently undercut by two issues: the evaluated model omits texture features that are part of the method description, and the gold-standard labels rely on an unvalidated multi-modal registration step. These are fixable but require clarification and additional evidence.

major comments (3)
  1. [Section II-C, Eq. (9), Fig. 4, Section III-C.2] The method section describes the MyoPS module as fusing motion, anatomy, and texture features, with Eq. (9) concatenating [Φ_i, L^a_i⊗Φ_i, I_i⊗Φ_i]. However, the feature effectiveness study in Section III-C.2 concludes that MyoPSΦL (motion and anatomy, without texture) is the best variant and states that motion and anatomy features were adopted for MyoPS in the following sections. Consequently, the test-set results in Table V and the clinical quantification in Section III-E correspond to a different model than the one specified in Eq. (9) and Fig. 4. Please revise the method description to match the evaluated model, or report both variants, and state explicitly in Eq. (9) which inputs are used by the final CineMyoPS model.
  2. [Section III-A, gold standard label generation] The gold-standard pathology labels are generated by registering rater-annotated LGE and T2w images to the cine ED frame with the MvMM tool, but no registration accuracy metric is reported anywhere in the paper. The reported inter-observer Dice values (0.69 for scar, 0.73 for edema) quantify label variability, not the accuracy of the LGE/T2-to-cine mapping. Given the slice-thickness mismatch at Center-R (LGE/T2 slice spacing 10–17 mm versus cine 6 mm in Table II), misregistration would directly bias the training targets and every test metric in Table V. Please add quantitative registration validation, such as Dice of transformed myocardial contours, target registration error on anatomical landmarks, or a documented visual inspection protocol, and specify how the registered LGE and T2w labels are fused into the final gold standard.
  3. [Section III-D, Table V] The claim that CineMyoPS outperforms the four baselines should be tempered. In Table V, the edema Dice differences between CineMyoPS (0.57) and OFSeg, ConvLSTM, and 2D+1D U-Net (0.55, 0.56, and 0.56, respectively) are not statistically significant, and the scar Dice gap to 2D+1D U-Net (0.53 versus 0.50) is also not marked as significant. The significant gains are mainly against nnU-Net and on specific metrics. Please soften the wording in Section III-D.1 and in the abstract, or provide an error analysis that explains where the method genuinely improves over the strongest baselines.
minor comments (5)
  1. [Table I] Center-R is listed with 50 subjects, but the TR/VA/TE row gives 0/0/45; please clarify the number of test subjects or correct the table.
  2. [Section I] In the last paragraph of the introduction, the stray word 'structure' appears before 'Overall'; this appears to be a typesetting artifact and should be removed.
  3. [Section II-B, Eq. (8)] The 'cos' operation in Eq. (8) is not explicitly defined; please define cosine similarity for the predicted probability maps and state whether it is computed per class or per channel.
  4. [Section II-A and Section III-A] The network is described as fully automatic, but the paper does not explain how the ED reference frame is selected during inference; if manual selection is required, please state this and discuss the possibility of automatic ED detection.
  5. [Section III-D.2, Table VI] Table VI compares reported Dice values from different datasets and evaluation protocols; the text notes dataset differences, but a table footnote should explicitly warn that the literature numbers are not directly comparable to the numbers reported in this paper.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: CineMyoPS is an empirical segmentation method evaluated against externally generated labels and independent baselines; the author's self-citations are procedural rather than load-bearing.

full rationale

The derivation chain in CineMyoPS is architectural and empirical rather than analytical, and no claimed prediction reduces to its inputs by construction. The gold-standard pathology labels are generated by registering LGE and T2w annotations to the cine ED frame with the MvMM tool and then fusing them; CineMyoPS is trained to predict these labels from cine images, so the target is external to the model's own outputs. Hyperparameters, frame proportion, and feature combination are selected on the validation set (Section III-C) before the Center-R test comparison, and the test results in Table V are computed against the same external gold standard with baselines trained under a common nnU-Net protocol, plus a human-observer reference. The consistency loss couples motion and anatomy features across frames but is supervised only by image similarity and the single ED anatomy label; it does not inject the pathology target into the features. Self-citations to the MyoPS benchmark and to MvMM are procedural: MvMM is used as a registration tool with assumptions that do not include the target segmentation, and the benchmark provides data rather than the paper's conclusion. No equation in the method section is equivalent to the training target by definition, and no fitted parameter is renamed as a prediction. Therefore the paper is self-contained against external evaluation and receives a score of 0.

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

The central contribution is empirical. The only hand-set numbers are the four loss weights and the frame proportion, all selected on the validation set. The key domain assumptions concern the informativeness of cine CMR, the accuracy of the fused gold standard, and the reliability of unsupervised motion estimates; none are proven by the paper itself. No new physical entities are introduced.

free parameters (5)
  • lambda_1 = 5
    Weight for the anatomy segmentation loss Lanatomy in Eq (12), set by coarse and fine search on the validation set in Section III-B.
  • lambda_2 = 2
    Weight for the consistency loss Lcons, set by validation search.
  • lambda_3 = 1
    Weight for the motion MSE loss Lmotion, set by validation search.
  • lambda_4 = 100
    Weight for the smoothness regularization Lsmooth, set by validation search.
  • frame_proportion = 4/6 of the cardiac cycle
    Chosen on the validation set in the frame-interval study (Section III-C.1) as the best balance of information and computation.
assumptions (4)
  • domain assumption Cine bSSFP images carry T2-weighted contrast sufficient for edema to be detectable.
    Motivates the entire joint scar/edema task; cited to [28] and [29] in the Introduction, but the paper's own low edema Dice (0.57) suggests the signal is weak.
  • domain assumption The fused LGE/T2 gold-standard labels registered to the cine ED frame are valid.
    Used for supervised training and evaluation in Section III-A; inter-observer Dice of 0.69/0.73 puts a ceiling on achievable performance.
  • domain assumption Unsupervised DDFs estimated with MSE and smoothness losses capture the motion that is informative for pathologies.
    Motion features are primary inputs to the MyoPS module (Eq 9); no ground-truth motion validation is provided.
  • domain assumption Anatomy can be propagated across frames through the consistency loss using only the ED label.
    The consistency loss in Eq (8) assumes that warped anatomy predictions should match the ED reference; this is a soft learned constraint, not a verified physical model.

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Cite this review

Pith. "Pith review of CineMyoPS: Segmenting Myocardial Pathologies from Cine Cardiac MR." pith.science (2026). https://pith.science/paper/YTJSXPSV

@misc{pith2026250702289,
  author       = {Pith},
  title        = {Pith review of: CineMyoPS: Segmenting Myocardial Pathologies from Cine Cardiac MR},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YTJSXPSV}},
  note         = {Machine review of arXiv:2507.02289}
}
read the original abstract

Myocardial infarction (MI) is a leading cause of death worldwide. Late gadolinium enhancement (LGE) and T2-weighted cardiac magnetic resonance (CMR) imaging can respectively identify scarring and edema areas, both of which are essential for MI risk stratification and prognosis assessment. Although combining complementary information from multi-sequence CMR is useful, acquiring these sequences can be time-consuming and prohibitive, e.g., due to the administration of contrast agents. Cine CMR is a rapid and contrast-free imaging technique that can visualize both motion and structural abnormalities of the myocardium induced by acute MI. Therefore, we present a new end-to-end deep neural network, referred to as CineMyoPS, to segment myocardial pathologies, \ie scars and edema, solely from cine CMR images. Specifically, CineMyoPS extracts both motion and anatomy features associated with MI. Given the interdependence between these features, we design a consistency loss (resembling the co-training strategy) to facilitate their joint learning. Furthermore, we propose a time-series aggregation strategy to integrate MI-related features across the cardiac cycle, thereby enhancing segmentation accuracy for myocardial pathologies. Experimental results on a multi-center dataset demonstrate that CineMyoPS achieves promising performance in myocardial pathology segmentation, motion estimation, and anatomy segmentation.

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Pith tools

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