Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI

Carlos Mart\'in-Isla; Cristian Izquierdo; Karim Lekadir; Miguel A. Gonz\'alez Ballester; Steffen E. Petersen; V\'ictor M. Campello

REVIEW 3 major objections 5 minor 16 references

Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read A U-Net trained on real multi-sequence MRI plus CycleGAN-synthesized LGE and rotated scar images reaches average Dice scores of 0.898 (LV), 0.810 (MYO), and 0.866 (RV) on the 40-patient test set, rivaling a network trained on five times…

desk verdict A credible challenge write-up with strong test Dice, but the central claim that synthetic and augmented training helps is selected on a one-patient validation set and never shown on the test set. read the letter →

arxiv 1909.01182 v2 pith:L3V2DIDD submitted 2019-09-03 eess.IV cs.CV

Víctor M. Campello , Carlos Martín-Isla , Cristian Izquierdo , Steffen E. Petersen , Miguel A. González Ballester , Karim Lekadir This is my paper · ORCID

classification eess.IVcs.CV

keywords lategadoliniumenhancementMRIcardiacsegmentationmulti-sequenceimagesynthesisCycleGANdataaugmentationU-Netscartissue

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

The paper asks whether scarce labeled late gadolinium enhancement (LGE) cardiac MRI can be stretched far enough to train a robust segmentation model. It answers yes, by feeding the segmenter not only LGE images but also the same patients' non-contrast bSSFP and T2 sequences, by synthesizing extra LGE images from bSSFP with a CycleGAN, and by rotating the myocardial wall within the LGE images so scar tissue appears at many locations. The best combined configuration outperforms single-sequence training and, with only five labeled LGE volumes, reaches average Dice scores of 0.898 (LV), 0.810 (MYO), and 0.866 (RV) on the 40-patient test set. The practical payoff would be accurate scar quantification in settings where LGE labels are scarce but cine and other routine sequences are abundant.

What carries the argument

The argument rests on three components. The first is CycleGAN, an unpaired image-to-image translation network that converts bSSFP cine images into synthetic LGE images while preserving the underlying cardiac geometry, so the bSSFP ground-truth contours can supervise LGE segmentation. The second is shape-guided scar augmentation: 50 landmarks placed around the epicardium and endocardium allow the myocardial wall—and the scar within it—to be rotated in 20 steps of 7.2 degrees, spreading scar locations across up to 144 degrees. The third is a modified U-Net with deep supervision in the upsampling path and a reduced number of filters after each upsampling to match the label count; each sequence is fed as a separate single-channel input so one set of weights learns across modalities. These components together let the model exploit multi-sequence information and enlarged training sets without any registration step.

What would settle it

Measure, on the same patients, the contour displacement between synthetic LGE images and their real LGE counterparts at end-diastole; if the mean displacement approaches or exceeds the LGE slice thickness (5 mm), or if a model trained only on synthetic LGE fails to beat a model trained only on real LGE on a common held-out test set, the claim that synthetic LGE provides valid boundary information is refuted.

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Extended reading notes

Core claim

The central claim is that LGE-MRI segmentation accuracy is improved by complementary information from non-contrast MRI sequences, provided through a single network that consumes each sequence as one input channel and does not require inter-sequence registration. The paper's best model—trained on all three sequences (LGE, bSSFP, T2), with synthetic LGE images generated by CycleGAN from bSSFP, and with shape-guided rotations of the myocardium that move scar locations around the wall—achieves average Dice scores of 0.898 (LV), 0.810 (MYO), and 0.866 (RV) on the 40-case challenge test set. In the authors' comparison, this is on par with a recent deep learning method trained on 25 labeled LGE volumes, five times more than the five used here. The authors also report that a model trained on synthetic LGE alone (Dice 0.809 for LV on the five labeled LGE volumes) far exceeds a model trained on bSSFP alone (0.503), which they take as evidence that the synthesized images carry useful information for LGE segmentation.

Load-bearing premise

The load-bearing premise is that the CycleGAN-generated synthetic LGE images keep the cardiac boundaries in the same place as the bSSFP images they were made from, so the bSSFP ground-truth contours are valid supervision for LGE segmentation; the paper supports this with qualitative examples and an indirect segmentation test rather than a direct measurement of boundary fidelity.

Editorial extensions

If this is right

A segmentation network trained on all three sequences plus synthetic LGE and scar-rotation augmentation outperforms, on the challenge validation set, every other configuration tested, including models trained on real LGE alone or on real multi-sequence data without synthesis.
With only five labeled LGE volumes, the method attains Dice scores similar to a recent deep learning LGE segmentation system trained on 25 labeled volumes, suggesting that synthesis and augmentation can substitute for a substantial amount of manual annotation.
Synthetic LGE images generated from bSSFP carry enough boundary information that a model trained on them alone segments the five labeled LGE volumes with a LV Dice of 0.809, well above the 0.503 obtained by training on the original bSSFP images.
Because the proposed pipeline does not register the sequences, it can be applied to multi-sequence cardiac data with differently aligned slices and consistently different boundary shapes.
Rotating the myocardial wall and scar within the LGE images reduces the risk of overfitting to a small number of scar locations, and in the qualitative examples it corrects segmentation errors that remain after adding synthetic images alone.

Reading between the lines

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

If synthetic LGE inherits bSSFP geometry, the same unpaired-translation recipe could extend to other contrast-enhanced modalities with scarce labels, such as delayed-enhancement CT, whenever a non-contrast sequence from the same anatomy is available.
The authors leave open whether the gain comes from faithful transfer of boundaries or from extra texture variation; a quantitative synthetic-to-real contour displacement measurement would settle which mechanism is doing the work.
A direct comparison of the rigid scar rotation used here against the proposed elastic deformations would test whether scar-location diversity, rather than global shape change, is the active ingredient of the augmentation gain.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Quick take: this is a plausible MS-CMRSeg challenge entry, and the reported test Dice (0.898 LV, 0.810 MYO, 0.866 RV) are solid given only five labeled LGE volumes for training. But the paper's central claim—that adding synthetic LGE and rotated-scar augmentation improves segmentation—is not actually demonstrated on unseen data. The authors pick configuration 8 because it scores best on a validation set drawn from a single LGE patient, then report test results only for that model. That is a real gap, and the paper's own conclusion says extensive validation is still to come.

What is new: the specific assembly of CycleGAN-based bSSFP-to-LGE synthesis plus landmark-guided rotation of the myocardium to redistribute scar locations. Each ingredient is established, but the combination for LGE-MRI segmentation is new, and the method description is clear enough to reproduce with effort.

The soft spots are in the evaluation. Table 2 compares a model trained on bSSFP versus one trained on synthetic LGE, but both are evaluated on the same five labeled LGE volumes that are part of the training data—so that comparison does not measure generalization. The eight-configuration ranking is done on 20% of those five volumes, essentially one patient, so the apparent gains from configurations 4 and 8 could easily be noise. No test-set Dice for configurations 1, 3, or 4 are reported, so we cannot tell whether the synthetic or augmentation steps actually generalize. The synthetic LGE fidelity is also only checked indirectly; the assumption that bSSFP contours are valid supervision for synthetic LGE is not verified quantitatively. These are not fatal to the pipeline, but they do make the improvement claim conditional.

The citation pattern is clean: standard references, no self-citation concern. The writing is clear and honest about limitations.

This paper is for readers working on cardiac MRI segmentation with limited annotated LGE data. They will find a useful template and a cautionary example of how easy it is to over-read validation-set rankings. It deserves a serious referee. I would accept it for peer review, but the revision should include test-set results for at least the LGE-only and the all-sequences-without-synthesis/augmentation variants, and model selection should use a larger validation set or cross-validation.

Referee Report

3 major / 5 minor

Summary. The paper proposes a deep-learning pipeline for segmenting the left ventricle, myocardium, and right ventricle in late gadolinium enhancement cardiac MRI (LGE-MRI), trained with only five labeled LGE volumes. To compensate for the small labeled dataset, the method augments the LGE training set in two ways: shape-guided rotations of the myocardium and LV to relocate scar tissue, and CycleGAN-based translation from bSSFP cine images into synthetic LGE images. A modified U-Net is then trained on eight configurations that combine real LGE, bSSFP, T2, synthetic LGE, and the scar-rotation augmentation. The authors select configuration 8 (all sequences plus synthetic LGE and rotations) based on validation performance and report its results on the 40-patient MS-CMRSeg test set, with average Dice scores of 0.898 (LV), 0.810 (MYO), and 0.866 (RV). The paper claims that multi-sequence training with synthetic images and augmentation improves LGE segmentation over conventional training with real LGE data only.

Significance. If the central claim holds, the paper would offer a practically valuable solution to the scarcity of annotated LGE-MRI data: it would show that non-contrast sequences and synthetic examples can substitute for large numbers of real LGE annotations. The use of an external challenge test set for the final model is a strength, as are the reported standard deviations and the comparison with a prior method trained on five times more LGE data. The work is also reproducible in style, as it identifies the public CycleGAN implementation and reports training details. However, the improvement claim is not currently established on unseen data, because test-set metrics are reported only for the selected configuration and the intermediate comparisons are evaluated on training volumes. The practical value therefore depends on additional experiments that the paper itself acknowledges are still needed.

major comments (3)

[Section 3, Table 3] The central claim that configuration 8 improves over conventional training is not supported by unseen-data evidence. Test-set Dice are reported only for the selected model (Table 3), and no test-set results are given for configurations 1, 2, 3, or 4, so the reader cannot verify the claimed improvement on the 40-patient test set. The only quantitative comparison between models trained with and without synthetic images (Table 2) is performed on the five labeled LGE volumes, which are part of the training data, not an independent test set. The paper's own conclusion in Section 4 states that 'extensive validation will be performed to assess in detail the relative importance of the different steps,' which confirms that the relative contribution of each component is not established in this manuscript. I would ask the authors to report test-set metrics for all eight configurations, or, if the challenge organizers cannot supply per-configuration test results, to provide a multi-fold cross-validation on the five labeled LGE patients and clearly separate training, validation, and test folds.
[Section 2.3, Section 3] The model selection among the eight training configurations is based on a validation set consisting of 20% of the five labeled LGE volumes, i.e., a single patient. Ranking eight configurations on one volume cannot reliably distinguish genuine improvements from noise, especially when the selection considers three Dice metrics simultaneously. The reported gains from synthetic images and scar-rotation augmentation may therefore reflect overfitting to that single validation volume rather than a generalizable effect. Please provide either a leave-one-patient-out cross-validation over the five labeled LGE volumes or, preferably, test-set results for all eight configurations so that the selection is not made on a single patient.
[Section 2.2, Table 2] The synthetic LGE images are evaluated only qualitatively (Figure 2) and indirectly through a segmentation experiment on the five labeled LGE volumes (Table 2). The paper assumes that the bSSFP ground-truth contours are valid supervision for the synthetic LGE images, but the anatomical fidelity of the synthetic images is not quantitatively verified. The reported Dice scores for the model trained with synthetic LGE (0.809 LV, 0.688 MYO, 0.820 RV) are not compared against an upper bound or against a model trained with real LGE images on a held-out set, so they do not establish that the synthetic images preserve cardiac boundaries well enough for LGE segmentation. Please add a quantitative measure of boundary fidelity (for example, boundary displacement between synthetic LGE and real LGE in matched slices, or a manual quality rating) and evaluate the synthetic-LGE ablation on unseen LGE data.

minor comments (5)

[Section 2.2] The CycleGAN training description would benefit from specifying the image size, normalization, and preprocessing used for the generators, since these details affect the quality of the synthetic LGE images.
[Figure 4] The columns in Figure 4 are not labeled with the corresponding configuration numbers (3, 7, 8); adding explicit labels or extending the caption would make the qualitative comparison easier to follow.
[Section 2.2] Please clarify whether the scar-rotation augmentation is applied only to real LGE images or also to synthetic LGE images, since configuration 4 and configuration 8 differ in exactly which images receive the rotations.
[Section 2.3] The sentence describing the reduction of filters after upsampling is a little ambiguous; it would be clearer to state that the number of filters in the final upsampling block is reduced to match the number of segmentation labels, as in the cited reference [16].
[Section 3] The comparison with the results of Yue et al. [3] is informal; please indicate whether the Dice scores quoted for that method come from the same MS-CMRSeg test set and whether any statistical significance testing was performed.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is an empirical multi-sequence segmentation pipeline with independent test evaluation, and no load-bearing claim reduces to its inputs by construction.

full rationale

The derivation chain in this paper is empirical rather than analytical. The central claim is that training a U-Net on multi-sequence inputs plus CycleGAN-synthesized LGE images and rotated scar augmentation improves LGE-MRI segmentation. The synthesized LGE images are generated from bSSFP inputs using an external CycleGAN implementation, and the segmentation model is then evaluated on an independently scored 40-patient MS-CMRSeg test set. The test Dice scores in Table 3 are not fitted constants and do not reduce by definition to any training input. Table 2 does compare models trained on bSSFP versus synthetic LGE using the five labeled LGE volumes, and these volumes overlap with the training/validation pool, which is a methodological limitation rather than circularity: the comparison is an internal experiment, not a prediction that is forced by construction. Configurations 1-8 are ranked on the validation set and only configuration 8 is reported on the test set; this is standard model selection practice, and the test set remains unseen during that selection. The paper's own conclusion that 'extensive validation will be performed to assess in detail the relative importance of the different steps' is a stated limitation, not evidence that the reported improvement is defined into existence. There are no self-citation chains invoked to justify the method's premises, no fitted parameter renamed as a prediction, and no uniqueness theorem imported from the authors' prior work. Therefore the appropriate finding is no significant circularity, with score 0.

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

No fitted constants or invented entities appear; the listed parameters are hand-chosen training choices, and the axioms are standard clinical-imaging assumptions plus the unverified synthetic-image fidelity assumption.

free parameters (3)

Number of scar-location rotations = 20
Chosen by hand to multiply the LGE dataset by 20; no sensitivity analysis is provided.
Rotation step size = 7.2 degrees
Chosen to cover scar locations up to 144 degrees clockwise; hand-picked.
Number of landmarks for epicardium/endocardium = 50
Used to define the rotation of the myocardium; fixed without justification.

assumptions (3)

domain assumption The provided ground-truth segmentations for bSSFP, T2, and the five labeled LGE volumes are accurate.
The method relies on these labels for training, augmentation, and validation.
domain assumption The CycleGAN-generated synthetic LGE images preserve the cardiac boundaries from the source bSSFP images.
This is the load-bearing assumption in Section 2.2, used to train with bSSFP labels on synthetic LGE.
domain assumption All sequences were acquired at end-diastole and are sufficiently aligned for cross-sequence learning.
Stated in Section 2.1; inter-sequence misalignment is acknowledged but the network is still trained with a single channel input.

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Cite this review

Pith. "Pith review of Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI." pith.science (2026). https://pith.science/paper/L3V2DIDD

@misc{pith2026190901182,
  author       = {Pith},
  title        = {Pith review of: Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/L3V2DIDD}},
  note         = {Machine review of arXiv:1909.01182}
}

read the original abstract

Accurate segmentation of the cardiac boundaries in late gadolinium enhancement magnetic resonance images (LGE-MRI) is a fundamental step for accurate quantification of scar tissue. However, while there are many solutions for automatic cardiac segmentation of cine images, the presence of scar tissue can make the correct delineation of the myocardium in LGE-MRI challenging even for human experts. As part of the Multi-Sequence Cardiac MR Segmentation Challenge, we propose a solution for LGE-MRI segmentation based on two components. First, a generative adversarial network is trained for the task of modality-to-modality translation between cine and LGE-MRI sequences to obtain extra synthetic images for both modalities. Second, a deep learning model is trained for segmentation with different combinations of original, augmented and synthetic sequences. Our results based on three magnetic resonance sequences (LGE, bSSFP and T2) from 45 different patients show that the multi-sequence model training integrating synthetic images and data augmentation improves in the segmentation over conventional training with real datasets. In conclusion, the accuracy of the segmentation of LGE-MRI images can be improved by using complementary information provided by non-contrast MRI sequences.

Figures

Figures reproduced from arXiv: 1909.01182 by the authors.

**Figure 1.** Example of three rotations of the myocardial wall with respect to the whole image by using the landmarks provided in the leftmost image. This shows the changes in the location of the scar tissues Image synthesis The rationale behind the proposed image synthesis is that there are many more segmented cine-MRI datasets available open-access or in clinical registries for training CNN models. Thus, to increase the number… view at source ↗

**Figure 2.** Examples of synthetic LGE-MRI images. The leftmost column are the original cine images, the central column shows the transformed images to the LGE domain and the rightmost column is the most similar slice from the real LGE sequences, since they were not registered/aligned. For the training of the CycleGAN model, all slices from the 45 patients for the LGE and bSSFP sequences were used during 200 epochs. The training… view at source ↗

**Figure 3.** Detailed architecture of the CNN model used for LGE segmentation. The numbers in the boxes correspond to the number of channels. Convolution operations have a kernel size of 3 × 3 and stride of 1, while transpose convolutions have a kernel size of 4 × 4 and stride of 2. During training, 20% of the patients for each dataset was reserved for validation and early stopping. With a batch size of 8 images, this model to… view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Three segmentation examples as obtained by using different training combinations, showing the improvement achieved by integrating inter-sequence image synthesis (column 3) and scar tissue augmentation (column 4) during training. methods. Firstly, since samples of anno…

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Reference graph

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Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

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