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REVIEW 3 major objections 6 minor 53 references

Cardiac Digital Twins at Scale from MRI: Open Tools and Representative Models from ~55000 UK Biobank Participants

T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read An automatic pipeline converts cine cardiac MRI into biventricular meshes for over 50,000 UK Biobank participants and releases 1,423 demographic representative meshes with fibers and coordinates.

desk verdict A genuinely useful large-scale resource from an honest pipeline, with a disclosed but unresolved RV wall-thickness assumption that limits but does not sink it. read the letter →

arxiv 2505.21019 v1 pith:HA5M2Y3Q submitted 2025-05-27 eess.IV cs.LG

Devran Ugurlu , Shuang Qian , Elliot Fairweather , Charlene Mauger , Bram Ruijsink , Laura Dal Toso , Yu Deng , Marina Strocchi
show 5 more authors
Reza Razavi Alistair Young Pablo Lamata Steven Niederer Martin Bishop
This is my paper
classification eess.IVcs.LG
keywords cardiacdigitalheartmeshesmodelsrepresentativeacrossavailable
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

Cardiac digital twins are computer models of a person's heart used to simulate disease and plan treatment. Building them normally requires an expert to manually turn MRI images into a 3D mesh, which is slow and hard to repeat across thousands of people. This paper describes a fully automatic pipeline: a neural network first outlines the heart chambers in the MRI slices, then a geometric method fits a biventricular mesh to those outlines, adds a realistic muscle fiber pattern, and maps each point to standard heart coordinates.

The authors ran this pipeline on about 55,000 UK Biobank participants who had heart MRI. They validated the automatic outlines against expert outlines, reporting Dice scores mostly above 0.88, and compared derived volumes and ejection fractions with previously published values. They then grouped participants by sex, age, and body mass index, and averaged the shapes within each group to create 1,423 representative meshes, which they will release publicly along with the code.

The main caveats are openly stated: the right ventricular wall thickness is fixed at 3 mm for everyone, the mesh-derived volumes run 4-7% below segmentation-derived volumes and mesh-derived left ventricular mass is about 20% higher, and the pre-trained networks and meshes are only promised 'soon', not yet downloadable. The representative meshes are averages, not individual patient models. Even so, the resource would let many research groups run cardiac simulations without rebuilding meshes from scratch.

Extended reading notes

Core claim

The paper's central claim is that an automatic open-source pipeline can generate patient-specific biventricular meshes from cine CMR images at scale, applied to roughly 55,000 UK Biobank participants, producing 1,423 representative meshes that constitute 'the most comprehensive cohort of adult heart models to date' (Abstract). If correct, researchers get open tools and a demographic mesh resource for cardiac digital twin studies.

Load-bearing premise

The pipeline assumes a fixed right ventricular wall thickness of 3 mm for every participant, extending the RV endocardium outward by this amount to create the RV epicardium (Methods, 'Finite element mesh construction'). The authors flag in Limitations that this 'can lead to inaccurate R V function estimation.' If real RV wall thickness varies substantially across the cohort, every released mesh carries an uncontrolled RV geometric bias.

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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 / 6 minor

Summary. This paper presents an automated pipeline that converts raw short- and long-axis cine CMR images from UK Biobank into biventricular tetrahedral meshes with fibers and universal ventricular coordinates, using nnU-Net segmentation, atlas-based surface reconstruction, and meshtool volume meshing. The pipeline is applied to 54,926 participants, of whom 46,917 pass quality control and contribute to 1,423 demographic-bin representative meshes spanning sex, age, and BMI. The authors validate segmentation Dice scores and derived phenotypes against manual segmentations and previously published values, and report demographic trends (sex differences, age-related decline, BMI-related increase in volumes and mass) that are consistent with the literature.

Significance. The resource is potentially valuable: the code is open source, the pipeline is demonstrated at a scale (>50k) not previously shown for mesh generation, the segmentation validation is technically sound (Dice mostly 0.88–0.98, phenotype errors comparable to prior U-Net work), and the representative meshes with fibers and UVCs would fill a genuine gap in publicly available cardiac digital twin cohorts. The paper is also commendably transparent about its limitations. If the systematic mesh biases and the fixed RV free-wall assumption are addressed or clearly scoped, this could become a standard baseline for population-scale cardiac digital twinning.

major comments (3)
  1. [Methods, 'Finite element mesh construction'] The RV epicardium is generated by uniformly offsetting the RV endocardium by 3 mm (Methods, 'Finite element mesh construction'; Limitations). This means every biventricular mesh and all 1,423 representative meshes carry a synthetic RV free wall not derived from the participant's images. Because the QC step compares mesh-derived to segmentation-derived volumes and mass, and the segmentations contain no RV epicardium, a uniform offset error is invisible to QC. The authors cite references [42,43] for the 3 mm value but report no sensitivity analysis or external validation of this parameter, and published normal RV free-wall thickness spans roughly 3–5 mm. For a resource intended for electro-mechanical simulations of RV stress, mass, and conduction, this is a load-bearing limitation; please either add a sensitivity analysis over the physiological range of RV wall thickness, validate against a dataset with RV wall measurements, or revise the 'patient-specific' claim to specify that the LV is image-derived while the RV free wall is a fixed-thickness estimate.
  2. [Table 2] Table 2 shows systematic mesh-versus-segmentation differences: LVEDV −7.7%, LVESV −8.0%, RVEDV −8.0%, RVESV −8.6%, and LV mass +18.6% relative difference (nnUNet–Mesh). The Discussion acknowledges this bias and states it is 'unclear' which phenotype should be preferred, but the bias propagates directly into the representative meshes and the demographic regressions in Fig 4 (e.g., LV mass 133.6±14.8 g for males, above the segmentation-derived values in Table 2). Since the central deliverable is a cohort for quantitative twin studies, the manuscript should either provide a calibration/correction for mesh-derived phenotypes, report which downstream quantities are robust to this bias (e.g., EF appears partly protected), or explicitly flag that representative-mesh phenotypes are not interchangeable with segmentation-derived clinical measurements.
  3. [Methods, 'Representative mesh generation for different sex, age and BMI groups'] The representative meshes are binned averages with no report of within-bin shape or phenotype variability, and the QC threshold (75th percentile plus 1.5 IQR across three phenotypes) and the minimum bin size of three are arbitrary choices with no sensitivity analysis. For the claim that these 1,423 meshes are 'representative' of demographic groups, please report the within-bin dispersion (e.g., standard deviation or percentiles of volumes and mass) and test whether the demographic trends in Fig 4 persist under alternative QC and binning choices. This would substantiate the representativeness claim and help users understand how much individual variation is lost by averaging.
minor comments (6)
  1. [Introduction] 'Steady state free precision' should be 'steady-state free precession'.
  2. [Abstract and Discussion] The Abstract says pre-trained networks and representative meshes 'will be made available soon', while the Discussion states they 'are made publicly available'; please reconcile this and give a concrete availability date or repository status for the meshes, fibers, and trained networks.
  3. [Table S2 and Methods] Table S2 reports a 'contour quality-control' step that removes 2,377 subjects, but the main text does not describe this QC step; please document it in Methods.
  4. [Segmentation validation] The LAX segmentation validation uses only 50 test participants; given the importance of LAX landmarks (valve planes, apex) for mesh construction, adding confidence intervals or a comparison on an independent LAX dataset would strengthen the validation.
  5. [Fig 4] The p-values and regression coefficients in Fig 4 are reported without confidence intervals; adding CIs would improve interpretability of the demographic regressions.
  6. [Abstract] The abbreviation 'UVC' is used without expansion; please define it at first use.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the pipeline is validated against independent manual segmentations and published phenotypes, and the fixed RV wall thickness is an acknowledged external assumption rather than a hidden fit.

full rationale

The paper's derivation chain runs from UK Biobank cine CMR, through nnU-Net segmentation, contour/landmark extraction, atlas-based surface meshing [19], tetrahedral meshing, fiber/UVC assignment, and bin-averaged representative meshes. Each stage is checked against external evidence: segmentation Dice scores against manual segmentations from [29] (Table 1); derived volumes/EFs against manual, nnU-Net, and the independent UNet method of [47] (Tables 2 and 3); and demographic trends of the representative meshes against previously published UK Biobank and external population data [19,30,49]. No claimed prediction is defined in terms of the quantity it is said to predict, and no fitted parameter is relabeled as a result. The only notable modeling choice, a fixed 3 mm RV wall thickness, is explicitly stated in 'Finite element mesh construction' and openly listed as a limitation ('we assumed a fixed 3 mm as the R V wall thickness, that can lead to inaccurate R V function estimation'); it is grounded in external experimental references [42,43], exposed as a configurable pipeline variable, and is not used to generate the validation phenotypes that support the paper's core claims. Self-citations to prior group work (e.g., [19]) are tool citations, not invoked uniqueness theorems, and the current outputs are independently validated in this manuscript. Thus no circular step reaches the threshold required by the review rules.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The central claim rests on standard segmentation and meshing tools, but several hand-chosen constants (RV wall thickness, QC threshold, bin minimum) shape the public cohort. No new physical entities are introduced.

free parameters (4)
  • RV wall thickness = 3 mm (fixed)
    Used to create RV epicardium by extending RV endocardium normal to the surface; assumed constant for all participants (Methods, Finite element mesh construction). Flagged in Limitations as a possible source of RV inaccuracy.
  • Mesh QC outlier threshold = 75th percentile + 1.5 IQR
    Participants with relative mesh-vs-segmentation phenotype difference beyond this threshold were excluded from representative mesh construction (Methods, Representative mesh generation). Affects cohort composition.
  • Minimum participants per demographic bin = 3
    Only sex, age, BMI bins with at least 3 participants produced a representative mesh, yielding 1,423 meshes (Results, Application: Representative meshes).
  • SAX slices used for ES selection = 5 mid-slices
    ES frame chosen as the time frame minimizing LV cavity voxels across LAX views and the five mid-SA slices (Methods, Automatic selection of ED and ES frames). Heuristic not quantitatively validated.
assumptions (6)
  • domain assumption Manual segmentations from [29] are reliable ground truth for training and validation.
    Used to train nnUNet segmentation networks and to compute Dice and phenotype validation (Methods, Segmentation; Results).
  • domain assumption The atlas-based mesh construction method [19] produces anatomically valid biventricular surfaces when driven by automatic segmentations at scale.
    The pipeline relies on this established method without re-validation on the large cohort (Methods, Finite element mesh construction).
  • domain assumption The fixed 3 mm RV wall thickness is representative for the UKBB cohort.
    Applied to all participants; derived from experimental measurements [42,43] but not personalized (Methods, Finite element mesh construction; Limitations).
  • domain assumption The first cine frame after R-wave detection corresponds to end-diastole in the UKBB protocol.
    Used to select the ED frame without per-participant validation (Methods, Automatic selection of ED and ES frames).
  • domain assumption Rule-based fiber orientation with alpha from 60 to -60 and beta from -65 to 25 reproduces realistic biventricular fiber architecture.
    Standard literature method [46] applied to all meshes without subject-specific validation (Methods, Finite element mesh construction).
  • domain assumption Procrustes averaging of aligned surface meshes yields a representative 'average heart' for each demographic bin.
    Used to construct representative meshes; assumes shape averaging is meaningful across participants (Methods, Representative mesh generation).

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Cite this review

Pith. "Pith review of Cardiac Digital Twins at Scale from MRI: Open Tools and Representative Models from ~55000 UK Biobank Participants." pith.science (2026). https://pith.science/paper/HA5M2Y3Q

@misc{pith2026250521019,
  author       = {Pith},
  title        = {Pith review of: Cardiac Digital Twins at Scale from MRI: Open Tools and Representative Models from ~55000 UK Biobank Participants},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HA5M2Y3Q}},
  note         = {Machine review of arXiv:2505.21019}
}
abstract

A cardiac digital twin is a virtual replica of a patient's heart for screening, diagnosis, prognosis, risk assessment, and treatment planning of cardiovascular diseases. This requires an anatomically accurate patient-specific 3D structural representation of the heart, suitable for electro-mechanical simulations or study of disease mechanisms. However, generation of cardiac digital twins at scale is demanding and there are no public repositories of models across demographic groups. We describe an automatic open-source pipeline for creating patient-specific left and right ventricular meshes from cardiovascular magnetic resonance images, its application to a large cohort of ~55000 participants from UK Biobank, and the construction of the most comprehensive cohort of adult heart models to date, comprising 1423 representative meshes across sex (male, female), body mass index (range: 16 - 42 kg/m$^2$) and age (range: 49 - 80 years). Our code is available at https://github.com/cdttk/biv-volumetric-meshing/tree/plos2025 , and pre-trained networks, representative volumetric meshes with fibers and UVCs will be made available soon.

Figures

Figures reproduced from arXiv: 2505.21019 by the authors.

Figure 1
Figure 1. Overview. An overview of the proposed pipeline. Reproduced by kind permission of UK Biobank ©. 3. Age, sex and body mass index (BMI) of participants. 4. Manual segmentations for the end-diastolic (ED) and end-systolic (ES) frames of the SAX view of 4788 participants. These manual segmentations were created in a previous study [29]. 5. Various derived phenotypes such as ventricle volumes and ejection fractions report… view at source ↗
Figure 2
Figure 2. Picking the ES frame. The LV volume transients (LV voxel count on each time frame divided by the maximum LV voxel count across time frames) of six example participants for all the views separately, and their sum across the views. For the SAX view, only the five mid-slices were used for the calculation. The ES frame was picked as the time frame that minimizes the sum across all views. Extraction of contours and landm… view at source ↗
Figure 3
Figure 3. Contour and landmark extraction from segmentations. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Demographic bins and associations with derived phenotypes. [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]
Figure 5
Figure 5. Figure 5: Examples of representative meshes for different demographic bins. [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.