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REVIEW 3 major objections 5 minor 40 references

Deep learning-based segmentation of T1 and T2 cardiac MRI maps for automated disease detection

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

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Pith's one-line read Automated myocardial T1/T2 map segmentation beats expert inter-observer agreement, and combining four statistical features per map in a random forest lifts disease detection to F1 = 92.7% from 66.7% for single cutoffs.

desk verdict Solid segmentation evaluation, but the disease-detection claim is circular because the reference standard is partly defined by the same T1/T2 features the classifier uses. read the letter →

arxiv 2507.00903 v1 pith:H3BPQ3HA submitted 2025-07-01 eess.IV cs.AIcs.CV

Andreea Bianca Popescu , Andreas Seitz , Heiko Mahrholdt , Jens Wetzl , Athira Jacob , Lucian Mihai Itu , Constantin Suciu , Teodora Chitiboi This is my paper · ORCID
classification eess.IVcs.AIcs.CV
keywords myocardiumdeeplearningT1mappingT2cardiacMRIsegmentationrandomforestdiseasedetectionparametric
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

Manual contouring of myocardial T1 and T2 maps is time-consuming and observer-dependent, and clinical decisions usually turn on a single average relaxation time compared with a fixed cutoff. This paper tests whether a deep learning model can replace the manual contouring and whether a machine-learning classifier can replace the single cutoffs. On 261 held-out T1/T2 maps, the segmentation model outlines the myocardium with an average Dice score of 85.4%, above the 81.6% agreement between two expert observers, while changing the extracted T1/T2 values by only about 1.75% on average. Feeding the lower quartile, median, upper quartile, and average of native T1 and T2 myocardial values into a random forest raises disease detection to F1 = 92.7% with 100% recall on the test set, significantly better than the best single-feature cutoff (F1 = 66.7%, $p<0.001$). If the results hold, automated analysis of T1/T2 maps could remove one source of variability from cardiac MRI and replace one-number thresholds with a more informative feature combination.

What carries the argument

The central object is the pair of myocardial masks produced by the DenseUnet segmentation model on native T1 and T2 maps, from which four statistics are computed per slice: the average, lower quartile, median, and upper quartile of myocardial pixel relaxation times, then averaged over slices per patient. DenseUnet is a U-Net with dense convolutional blocks, pre-trained on 10,065 T1-weighted images and fine-tuned on 877 T1/T2 maps, and the whole pipeline runs in about 0.4 seconds per patient. These eight per-patient features are the entire input to the disease classifiers. The mechanism carrying the argument is the contrast between using each feature alone through a Youden-J-optimized cutoff and using all features together in a standard machine-learning classifier: that comparison isolates feature combination, rather than segmentation quality or algorithm choice, as the source of the F1 gain.

What would settle it

Re-classify the same 29 test subjects after re-defining the disease label without any cardiac MRI information, for example biopsy-confirmed myocarditis and biopsy-confirmed sarcoidosis only, then retrain the random forest on the same eight T1/T2 features and compare the test-set F1. If it drops near the 66.7% single-cutoff level, the reported gain reflects label-feature overlap rather than diagnostic ability. A second complementary check is a multi-center evaluation, since T1/T2 values shift across scanners and protocols and the paper itself identifies per-center recalibration as necessary.

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Extended reading notes

Core claim

The paper claims that a fully automated segment-and-classify pipeline is feasible and diagnostically useful for inflammatory and infiltrative myocardial disease. Its first claim is segmentation quality: a DenseUnet pre-trained on T1-weighted images and fine-tuned on T1/T2 maps yields a myocardial Dice score of 85.4% overall, exceeding the 81.6% Dice between the two independent expert observers on the same test set, and the model's extracted T1/T2 features correlate with manual features above 0.9. The second claim is that disease detection improves when multiple statistical features are combined: on the held-out test subset, every single-feature threshold performs poorly (best F1 66.7%), while a random forest trained on all eight native T1/T2 features (average, lower quartile, median, upper quartile for each map) reaches F1 = 92.7%, precision 86.4%, and recall 100%, a significant improvement over the cutoff baseline ($p<0.001$). The paper also claims classifier choice matters little, since logistic regression, SVM, Perceptron, and random forest all exceed F1 = 85%, so the gain comes from combining features rather than from the particular algorithm.

Load-bearing premise

The load-bearing premise is that the reference "diseased" labels are independent of the input features: myocarditis was defined by clinical criteria plus at least one cardiac MRI abnormality (oedema or late gadolinium enhancement), and sarcoidosis was included on the strength of abnormal T1/T2 mapping values, so a classifier fed T1/T2 maps may partly be predicting its own label.

Editorial extensions

If this is right

  • A myocardium contour from the model overlaps a second expert's contour about as well as two experts overlap each other, so manual delineation of T1/T2 maps can be replaced or cross-checked automatically.
  • Using all four statistics from native T1 and T2 maps in a random forest yields 100% recall on the 29-subject test set, meaning no known diseased subject was missed, with precision 86.4%.
  • Single-feature cutoffs are the weak link: the best T1 quartile cutoff reaches only F1 = 66.7%, while the same features combined in a logistic regression already reach F1 = 90.0%.
  • The pipeline runs in about 0.4 seconds per patient, so adding it to the reading workflow costs essentially no time.

Reading between the lines

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

  • If the quartile results transfer, disease may appear first in the tail of the myocardial relaxation-time distribution rather than its center: the T1 upper-quartile feature had the highest single-feature AUC (75.2%), so histogram or voxel-level features are a natural extension beyond four summary statistics.
  • The 100% recall figure comes from a 29-subject test set and a label definition that already includes cardiac MRI abnormalities; in a larger, biopsy-defined cohort the precision-recall balance would likely shift, and the paper's own single-center, single-scanner limitation should be read as a bound on generalizability.
  • Because T2 features underperformed despite better T2 segmentation, improving T2 map signal-to-noise, through acquisition or post-processing, may do more for disease detection than further improving contour accuracy.
  • The same segment-then-classify recipe could be applied to post-contrast T1 maps or extracellular-volume maps, which the paper leaves as future work; whether quartile features help there is untested.
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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 evaluates a DenseUNet for automatic segmentation of the left-ventricular blood pool and myocardium on native/post-contrast T1 and T2 maps, pretrained on public T1-weighted data and fine-tuned on 144 subjects. On a 261-map test set annotated by two observers, the model's average myocardial Dice score (85.4%) exceeds inter-observer agreement (81.6%), with mean absolute percentage error of 1.75% in T1/T2 values. From the automatic masks, the authors extract average, lower-quartile, median, and upper-quartile T1/T2 features and compare Youden-optimal single-feature cutoffs with five machine-learning classifiers for a binary normal-versus-diseased task; a random forest using all features achieves F1=92.7% and recall=100%, reported as significantly better than the cutoff approach. The authors conclude that deep learning facilitates segmentation and that combining multiple statistical features with machine learning improves disease detection. The segmentation claim is well supported by the evaluation; the disease-detection claim is weakened by a reference-standard definition that overlaps with the classifier inputs and by the very small test set.

Significance. If the segmentation result is taken on its own, the paper makes a credible and useful contribution: model-observer Dice exceeding inter-observer Dice across all map types, with Bland-Altman and Pearson correlation analyses showing that automatic contours have minimal impact on feature quantification, is a meaningful benchmark for automated T1/T2 mapping pipelines. The speed of the pipeline (0.4 s per patient) is also practically relevant. The machine-learning classification comparison, however, does not currently provide valid evidence for the headline claim of improved disease detection, because the 'diseased' label is partly defined by the same T1/T2 mapping abnormalities that the classifier uses as features. The feature-combination idea (quartile statistics plus ML) is worth testing, but the present evaluation conflates label construction with diagnostic accuracy. With an independent label definition and appropriate uncertainty quantification on a larger test set, the classification component would be significant; as it stands, the contribution is mainly the segmentation evaluation.

major comments (3)
  1. [Data and Disease Detection (pp. 7-10)] The binary label 'diseased' is not independent of the classifier features. The 'normal cardiac MRI' group is defined as having 'no fibrosis or oedema according to T1/T2 mapping or LGE imaging,' and the 'myocarditis' group requires at least one cardiac MRI abnormality, including myocardial oedema, which is directly assessable from T1/T2 mapping. Because the random forest in Table 5 is trained on native T1/T2 features, the reported F1=92.7% and recall=100% may largely reflect the classifier reproducing the oedema/fibrosis criterion embedded in the label rather than detecting disease independently of CMR. The sarcoidosis and systemic-disease subgroups are labeled without requiring myocardial involvement and are therefore potentially independent, but they are pooled with myocarditis, so their contribution cannot be separated. Please re-run the classification with labels that do not incorporate T1/T2 mapping findings, or, if that is not possible, report subgroup analyses for sarcoidosis and systemic disease separately and explicitly reframe the claim as detection of CMR-defined abnormality rather than disease detection.
  2. [Table 5 and Statistical Analysis (pp. 12-16)] The classification evaluation rests on only 29 test patients (10 normal, 19 diseased from Table 1), yet F1, precision, and recall are reported as point estimates without confidence intervals, and the Wilcoxon comparisons between classification approaches are not described in terms of the observations entering the test. With 29 patients, one or two reclassified cases materially change F1; moreover, the 'best' feature combination for each classifier was selected on a 15-patient validation subset, so the reported test performance is conditional on that selection. Please report per-patient predictions, bootstrap or exact binomial confidence intervals, and a precise description of the statistical test (e.g., McNemar on paired predictions or a permutation test over patients), and clarify how the p-values in Table 5 were computed.
  3. [Feature selection and comparison of methods (pp. 11-16)] The claim that machine learning improves disease detection over single cutoffs is based on comparing the best-performing random forest (selected after trying five classifiers and several feature combinations) with the best threshold method (T1 UQ), rather than a pre-specified comparison. Because multiple configurations were examined, the reported p-values do not account for selection or multiple comparisons. Please report the full set of configurations tried, or use a nested cross-validation or a pre-registered decision rule, so that the 'ML improves over cutoff' claim is not an artifact of best-of-many selection.
minor comments (5)
  1. [Eq. 3, Statistical Analysis] The MAPE formula as printed lacks the absolute value; it should be |G - M| / G x 100, otherwise negative errors are possible and the metric is not a true percentage error.
  2. [Data acquisition, p. 7] The text says 'flip angle 35° for T1 and 70° for T1'; the second should presumably be '70° for T2'.
  3. [Results, p. 15] In the sentence listing T2 best performers, 'T1 A (AUC = 62.6%)' appears to be a typo for 'T2 A (AUC = 62.6%)'.
  4. [Table 3, Mapping Segmentation] The reported 'average DICE of 85.4% ± 1.6%' is not directly shown in Table 3; please clarify that it is the mean of the Model-vs-Observer1 and Model-vs-Observer2 myocardial Dice values, and state the corresponding standard deviation over the same units.
  5. [Feature Analysis and Disease Detection, p. 11] Please specify how many test patients have both native T1 and T2 maps, since the random forest using 'all features' requires T2 features for every patient; if any values are missing, describe the imputation or exclusion rule.

Circularity Check

1 steps flagged · score 6.0 of 10

Disease-detection claim is circular because the diseased/normal labels are defined using T1/T2-mapping and LGE abnormalities, while the classifier inputs are T1/T2-mapping features.

  1. self definitional [Materials and Methods, Data (Dataset A definitions)]
    "The 'normal cardiac MRI' group included asymptomatic individuals with no known cardiovascular disease and a normal cardiac MRI according to expert reading (i.e. normal ejection fraction, normal left ventricular dimensions, no fibrosis or oedema according to T1/T2 mapping or LGE imaging). ... The 'myocarditis' group included patients with ≥1 clinical myocarditis criteria and ≥1 cardiac MRI abnormality (myocardial oedema or LGE with myocarditis pattern)."

    The disease-detection experiment feeds native T1/T2 statistical features (A, LQ, M, UQ) into a random forest to separate 'diseased' from 'normal'. But the 'normal' label is defined as having no fibrosis or oedema according to T1/T2 mapping or LGE, and the 'myocarditis' label requires a cardiac MRI abnormality (myocardial oedema or LGE). Therefore the target labels are partially constructed from the same T1/T2-mapping abnormality information used as predictor inputs. The reported F1=92.7% and the comparison with cutoff methods can largely be read as reproducing the paper's own inclusion criterion—detecting CMR-visible oedema/fibrosis—rather than as an independent test of diagnostic accuracy.

full rationale

The segmentation claim is self-contained: DICE/MAPE are computed against two independent manual annotations on a held-out test set, with no fitted parameter renamed as a prediction, so no circularity is found there. The ML classifiers are trained on the train split, tuned on validation, and evaluated on held-out test data; the cutoff ROC analysis on train+validation and test evaluation is a conventional split. The single load-bearing circularity is in the disease-detection target definition: 'normal' is defined by absence of fibrosis/oedema according to T1/T2 mapping or LGE, and 'myocarditis' requires a CMR abnormality (oedema or LGE). Since the inputs are T1/T2 map features, the classifier is in part predicting its own labeling criterion. This inflates the reported F1 and makes 'disease detection' partly a restatement of the inclusion criteria; however, it is not a case of fitted parameters being called predictions, and the sarcoidosis/systemic subgroups retain some independent content, so the circularity is partial rather than total. Score 6 reflects that the central disease-detection claim reduces, at least in part, to the definition of the labels.

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

The paper introduces no new physical entities or theoretical constructs. Its free parameters are standard ML hyperparameters and the cutoff thresholds fitted to training/validation data. The key assumptions are domain-level: label accuracy, ground-truth quality, transferability of pretraining, and the diagnostic informativeness of the chosen features. The most notable assumption is the overlap between myocarditis label definition and the T1/T2 input features.

free parameters (4)
  • Youden-optimal cutoff thresholds for each feature = T1 A 989 ms, T1 LQ 942 ms, T1 M 988 ms, T1 UQ 1034 ms, T2 A 54 ms, T2 LQ 49 ms, T2 M 52 ms, T2 UQ 57 ms
    Cutoffs derived from ROC analysis on combined training and validation subsets, then applied to the test set. They are the basis of the threshold-based classification baseline.
  • DL training hyperparameters = learning rate 3e-4, batch size 2, dropout 20%, 100 pretrain + 100 finetune epochs
    Chosen empirically; the segmentation accuracy depends on these choices, though standard for U-Net training.
  • ML classifier hyperparameters = not reported
    Selected via grid search maximizing validation F1, but specific values (e.g., number of trees in RF, C for SVM, k for KNN) are not listed in the paper.
  • Feature combinations per classifier = e.g., RF uses all 10 features; logistic regression uses [T1 LQ, UQ] or [T1 A, LQ, M]
    If the best combination was chosen based on test-set performance, this introduces selection bias and acts as a free parameter.
assumptions (5)
  • domain assumption The clinical labels (normal, myocarditis, sarcoidosis, systemic disease) are accurate
    The paper relies on clinical criteria and expert reading for the reference standard; no independent verification is provided.
  • domain assumption Manual segmentations by two observers are an acceptable ground truth for training and evaluation
    The DL model is trained on these annotations and evaluated against them; observer bias is treated as the benchmark.
  • domain assumption The Lake Louise criteria (including CMR abnormalities) define myocarditis
    Used for the diseased label; this creates partial overlap between input features and reference standard.
  • domain assumption Pretraining on T1-weighted images transfers to T1/T2 mapping segmentation
    The model is pretrained on Dataset B (T1-weighted images) and fine-tuned on maps; the transferability is assumed.
  • domain assumption The statistical features (A, LQ, M, UQ) capture diagnostically relevant information
    This is the premise of the feature analysis, partly evaluated by the AUC results.

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Cite this review

Pith. "Pith review of Deep learning-based segmentation of T1 and T2 cardiac MRI maps for automated disease detection." pith.science (2026). https://pith.science/paper/H3BPQ3HA

@misc{pith2026250700903,
  author       = {Pith},
  title        = {Pith review of: Deep learning-based segmentation of T1 and T2 cardiac MRI maps for automated disease detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/H3BPQ3HA}},
  note         = {Machine review of arXiv:2507.00903}
}
read the original abstract

Objectives Parametric tissue mapping enables quantitative cardiac tissue characterization but is limited by inter-observer variability during manual delineation. Traditional approaches relying on average relaxation values and single cutoffs may oversimplify myocardial complexity. This study evaluates whether deep learning (DL) can achieve segmentation accuracy comparable to inter-observer variability, explores the utility of statistical features beyond mean T1/T2 values, and assesses whether machine learning (ML) combining multiple features enhances disease detection. Materials & Methods T1 and T2 maps were manually segmented. The test subset was independently annotated by two observers, and inter-observer variability was assessed. A DL model was trained to segment left ventricle blood pool and myocardium. Average (A), lower quartile (LQ), median (M), and upper quartile (UQ) were computed for the myocardial pixels and employed in classification by applying cutoffs or in ML. Dice similarity coefficient (DICE) and mean absolute percentage error evaluated segmentation performance. Bland-Altman plots assessed inter-user and model-observer agreement. Receiver operating characteristic analysis determined optimal cutoffs. Pearson correlation compared features from model and manual segmentations. F1-score, precision, and recall evaluated classification performance. Wilcoxon test assessed differences between classification methods, with p < 0.05 considered statistically significant. Results 144 subjects were split into training (100), validation (15) and evaluation (29) subsets. Segmentation model achieved a DICE of 85.4%, surpassing inter-observer agreement. Random forest applied to all features increased F1-score (92.7%, p < 0.001). Conclusion DL facilitates segmentation of T1/ T2 maps. Combining multiple features with ML improves disease detection.

Discussion (0). Continue with ORCID to comment.

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Pith tools

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