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REVIEW 4 major objections 6 minor 73 references

Aneumo: A Large-Scale Multimodal Aneurysm Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read The paper presents Aneumo, an open dataset of 85,280 CFD-simulated pressure and velocity fields from 10,660 aneurysm geometries, and reports that a DeepONet-SwinT model predicts those fields on unseen real geometries more accurately than…

desk verdict A genuinely useful large open CFD dataset for aneurysm ML, but the synthetic geometry realism is under-validated and the flow-condition counts are internally inconsistent. read the letter →

arxiv 2505.14717 v1 pith:KO2MZKGK submitted 2025-05-19 eess.IV cs.AIcs.CVcs.LG

Xigui Li , Yuanye Zhou , Feiyang Xiao , Xin Guo , Chen Jiang , Tan Pan , Xingmeng Zhang , Cenyu Liu
show 11 more authors
Zeyun Miao Jianchao Ge Xiansheng Wang Qimeng Wang Yichi Zhang Wenbo Zhang Fengping Zhu Limei Han Yuan Qi Chensen Lin Yuan Cheng
This is my paper · ORCID
classification eess.IVcs.AIcs.CVcs.LG
keywords intracranialaneurysmhemodynamicscomputationalfluiddynamicsdatasetoperatorlearningDeepONetSwinTransformermultimodalmedicalimagingsurrogatemodels
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

This paper tries to establish that a new open dataset, Aneumo, closes a structural gap in aneurysm research: enough paired geometry-hemodynamics data to train machine-learning surrogates that can replace hours-long CFD simulations. Starting from 427 real intracranial aneurysm geometries, the authors remove the aneurysm, apply randomized polygon-offset deformations to synthesize 10,660 shapes, and simulate blood flow in each under eight steady mass-flow conditions, producing 85,280 pressure and velocity field samples together with segmentation masks, 3D surface meshes, and point-cloud representations. On a benchmark built from these data, the paper reports that a DeepONet model augmented with a Swin Transformer geometric encoder predicts pressure and velocity fields on held-out real geometries with lower normalized error and tighter error spread than the plain DeepONet baseline. If these claims hold, the dataset would make fast, geometry-based hemodynamic prediction practical, turning a per-case cost of roughly 45 minutes to 2 hours of supercomputer time into about 0.01 seconds of GPU inference, and providing a common resource for studying how morphology, flow, and rupture risk connect.

What carries the argument

The argument runs on three coupled mechanisms. First, geometry expansion: each real aneurysm is stripped to a healthy vessel, and a polygon-offset command displaces the wall along local normals by a randomly sampled distance (0.5-1.0 in model units), with transition bands for smooth blending, yielding 10,660 synthetic shapes that are then voxelized into NIfTI masks. Second, flow-field generation: the shapes are meshed with unstructured polyhedral cells and ten prismatic boundary layers, and steady incompressible Navier-Stokes equations are solved in OpenFOAM with the icoFoam solver and PISO pressure-velocity coupling at eight mass-flow rates between 0.001 and 0.004 kg/s, with residuals driven to convergence and a mesh-sensitivity check at 0.15 mm. Third, the machine-learning surrogate: the benchmark object is DeepONet-SwinT, which keeps DeepONet's branch-trunk operator decomposition but replaces the purely MLP geometry encoding with a Swin Transformer that reads the 3D mask, adds a boundary-condition branch and a mass-flow scaler, and outputs pressure, the three velocity components, and pressure difference.

What would settle it

Take Aneumo's synthetic shapes and the 427 parent geometries and run a simple supervised classifier on standard aneurysm morphometrics (volume change rate, aspect ratio, sphericity, neck-to-dome ratio): near-perfect separation would demonstrate distributional mismatch in synthetic shapes. A more direct test is to retrain the DeepONet-SwinT benchmark on the subset of synthetic shapes with volume change rate below 1 and evaluate on the held-out real geometry test set; if the reported accuracy comes mostly from easy, low-deformation cases, the errors on realistic extremes would reveal that the current benchmark overstates generalization.

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Extended reading notes

Core claim

The central claim is that Aneumo is the first large-scale, high-fidelity, multimodal aneurysm dataset: one public resource that combines 10,660 3D aneurysm models, 10,660 binary segmentation masks, dense point clouds, CFD meshes, and 85,280 simulated velocity/pressure fields spanning eight physiologically motivated inflow rates. The dataset is derived from 427 real aneurysm models by a deaneurysm-and-deform pipeline meant to mimic aneurysm evolution, with each parent shape subjected to at least 20 randomized non-rigid stretch transformations. The paper further claims that a benchmark on this dataset shows an operator-learning architecture, DeepONet-SwinT, learns the geometry-to-hemodynamics mapping well enough to generalize to completely unseen real aneurysm geometries, with lower MNAE and L2 errors, faster convergence, and a more concentrated error distribution than the standard DeepONet, at an inference cost of about 0.01 seconds per case. The authors present these results as evidence that data-driven hemodynamic surrogates are viable for aneurysm research and, eventually, clinical risk assessment.

Load-bearing premise

The load-bearing assumption is that aneurysm shapes produced by randomly offsetting the vessel wall by 0.5-1.0 units resemble real aneurysms closely enough that models trained on them transfer to clinical data; the paper's only check is neurosurgeon review, and its own Section 3.4 reports volume-change-rate outliers up to 3.5, indicating some generated shapes are physiologically extreme.

Editorial extensions

If this is right

  • If Aneumo is as usable as claimed, training surrogate models on it turns a per-case CFD pipeline that takes 45 minutes to 2 hours on 24 CPU cores into GPU inference of about 0.01 seconds per case.
  • The multimodal format (masks, STL meshes, VTK fields, NPY arrays) lets the same dataset support segmentation, 3D reconstruction, point-cloud learning, and flow-field prediction tasks without re-formatting.
  • The benchmark's geometry-disjoint splits and independent real-geometry test set provide a common protocol for comparing operator-learning and SciML methods on aneurysm hemodynamics.
  • The scaling experiment, in which increasing training data from 1,280 to 12,800 cases lowers validation error for both models, suggests that continued expansion of the dataset would further improve surrogate accuracy.
  • The eight flow-rate sweep can be used to train models that interpolate across physiological inflow conditions, which the paper's validation-set-diversity experiments show matters more than point density for generalization.

Reading between the lines

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

  • The authors did not separate the effect of deformation extremes: a natural follow-up is to retrain surrogates with the anomalous shapes (volume change rate above, say, 2.0) removed and compare test error on real geometries; this would show whether the tail helps or hurts transfer.
  • Because only steady-state flow fields are provided, any clinical claim about rupture risk would need extra validation against transient simulations, since pulsatile wall shear stress and oscillatory indices are absent from the dataset and the paper itself lists steady-state as a limitation.
  • The paired geometry-flow format invites generative tasks the paper does not demonstrate, such as learning a conditional model of aneurysm growth from hemodynamic fields or using flow predictions to guide shape editing; testing these tasks on Aneumo would extend its value beyond surrogate prediction.
  • A cheap sanity check of the synthetic shapes is a one-class morphometry comparison: if a simple classifier separates the 10,660 synthetic shapes from the 427 parent shapes using size- and shape-normalized statistics with near-perfect accuracy, the deformation distribution is a weak model of natural aneurysm evolution, and clinical transfer should be scrutinized.
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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

4 major / 6 minor

Summary. The paper introduces Aneumo, a dataset of 10,660 synthetic intracranial aneurysm geometries derived from 427 real AneuX geometries, together with 85,280 CFD solutions (velocity and pressure fields) generated under a nominal set of eight steady-state mass-flow conditions, segmentation masks, and supporting mesh and VTK/NumPy outputs. The authors also present a benchmark comparing a standard DeepONet with a proposed DeepONet-SwinT architecture for predicting hemodynamic fields, reporting improved accuracy and efficiency on a 20-case test set drawn from AneuX.

Significance. If the dataset counts and the independence claims are verified, Aneumo would be a valuable community resource: it is among the largest open collections pairing aneurysm geometry with field-level CFD hemodynamic data, and it provides multimodal outputs (masks, point clouds, 3D models, flow fields) plus an operator-learning benchmark. The paper also demonstrates a concrete baseline and a scale-up analysis. However, the scientific value depends on two load-bearing points that currently need correction: the exact number of distinct flow conditions and the actual independence of the test set from the training-generation source.

major comments (4)
  1. [Section 3.3 and Appendix A.3] The number of distinct mass-flow conditions is inconsistent. Section 3.3 lists eight values including both 0.0030 and 0.003 kg/s, which are numerically the same value, while Appendix A.3 lists seven distinct values: 0.0010, 0.0015, 0.0020, 0.0025, 0.0030, 0.00375, and 0.0040 kg/s. Since the headline count 85,280 equals 10,660 × 8, the presence of a duplicate means the actual number of unique CFD cases would be 10,660 × 7 = 74,620 unless simulations were intentionally repeated under nominally identical conditions. This discrepancy affects the abstract, the contribution list, Table 1, Section 3.3, and Appendix A.3. The authors should state the exact number of distinct flow conditions, recompute all dataset counts, and correct the statement in Appendix A.3 that 85,280 simulations were completed for each three-dimensional model (that number is the total across all models, not per model).
  2. [Section 4 (benchmark test set)] The test set is not independent of the training-generation pipeline. The paper claims the test set is "completely independent" because it is drawn from AneuX, but the training geometries in Aneumo are themselves generated from AneuX real geometries (Section 3.1 and Appendix A.1). The 10 test geometries may therefore be the same underlying patient geometries from which synthetic training shapes were deformed, which can inflate the reported generalization performance. The authors need to demonstrate, with a concrete mapping or hash-based geometry check, that no test geometry or its deformed descendants appear in the training/validation sets, or use an external dataset for the test set.
  3. [Appendix A.1 and Section 3.4] The physiological plausibility of the synthetic aneurysm shapes is not established. The deformation method is a single polygon-offset operation with distance d randomly sampled from [0.5, 1.0], which produces a smooth radial bulge; this does not reproduce the asymmetric, blebbed, or daughter-sac morphologies typical of clinical aneurysms. The only stated validation is that neurosurgeons confirmed the shapes (Section 3.1), with no inter-rater reliability, blinding, or quantitative morphological comparison to real aneurysms. Section 3.4 further reports volume change rates reaching 3.5 and labels these as anomalies, but no threshold, count, or exclusion rule is given. The authors should add a quantitative morphological analysis (e.g., size, aspect ratio, surface irregularity distributions compared with a clinical cohort) and either exclude or separately analyze the out-of-distribution shapes so that downstream users can judge clinical transferability.
  4. [Section 3 and Appendix A.1] The geometry-generation protocol is not described reproducibly. Section 3 states that each baseline model undergoes "at least 20 randomized non-rigid stretching transformations," but Appendix A.1 describes only one polygon-offset deformation with a random distance. To produce 10,660 shapes from 427 baselines, an average of about 25 outputs per baseline is needed, yet the text does not specify how many deformation trials are performed per geometry, whether the 20 transformations are a lower bound, or how the final count 10,660 was reached. This must be clarified in the supplementary material, along with the exact parameters of the transition band and the post-deformation smoothing steps.
minor comments (6)
  1. [Section 3.2] The phrase "withth high accuracy" contains a typo; it should read "with high accuracy."
  2. [Section 3.3] The dataset name is misspelled as "Anuemo" in the sentence "A total of 85,280 CFD simulations were completed for each 3D model in the Anuemo dataset."
  3. [Appendix A.3] The text says "105 iterations" where the intended meaning is clearly 10^5 iterations; please use proper scientific notation.
  4. [Appendix A.3 and Figure 8] The text says "as might be illustrated in a Figure 8, not provided here," but Figure 8 is in fact provided below; this internal contradiction should be removed.
  5. [References] References [24] and [25] are duplicates of the same work and should be merged or replaced with distinct citations.
  6. [Appendix B.3.2] The scaling experiments in Figures 14 and 15 show 1000 epochs for DeepONet-SwinT, while the main text and earlier figures report 5000 epochs; the training durations should be stated consistently.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the dataset and benchmark are self-contained and no prediction reduces to a fit or to a self-citation chain.

full rationale

This is a dataset-construction paper, not a derivation paper, and I found no step in which an output is equivalent to an input by construction. The synthetic geometries are produced by explicit polygon-offset deformation of AneuX models (Appendix A.1) and then fed into independent OpenFOAM CFD simulations; the resulting velocity and pressure fields are new outputs, not refitted versions of the deformation parameters. The benchmark trains DeepONet and DeepONet-SwinT on Aneumo CFD data and evaluates on real AneuX geometries; no parameter is fitted to the test set and no test quantity is re-derived from the training labels. The geometric-authenticity concerns raised by the volume-change-rate anomalies and the subjective neurosurgeon review are validity and transferability risks, not circular reasoning. The only self-referential element is that the benchmark test set comes from AneuX, the same source family used to create the synthetic training geometries; this is a potential data-leakage or evaluation-bias issue, not a by-construction reduction, and therefore does not raise the circularity score.

Assumptions & free parameters 3 free parameters · 6 assumptions · 0 invented entities

The central dataset claim rests on standard CFD modeling assumptions plus a deformation pipeline that is not independently validated. The free parameters are design choices rather than fitted constants, but they shape the dataset's fidelity claims.

free parameters (3)
  • Minimum mesh size = 0.15 mm
    Chosen from mesh sensitivity analysis (Appendix A.2); the high-fidelity claim depends on this resolution for all 10,660 geometries, including extreme deformations.
  • Deformation offset distance d = Randomly sampled from [0.5, 1.0]
    Randomly sampled offset in Geomagic Wrap to create synthetic aneurysms; central to generating 10,660 shapes, but the range is a hand-picked design choice.
  • Inlet mass flow rates = 0.0010 to 0.0040 kg/s (7 or 8 discrete values; inconsistent in text)
    The set of steady flow rates is a chosen design choice that defines the dataset size (85,280 = 10,660 × 8), but the paper lists a duplicate value in Section 3.3 and only seven values in Appendix A.3.
assumptions (6)
  • standard math Navier-Stokes equations for incompressible Newtonian fluid govern blood flow in intracranial aneurysms.
    Used in Section A.3 as governing equations; standard continuum mechanics assumption for this scale.
  • domain assumption Blood can be modeled as incompressible Newtonian fluid with density 1050 kg/m^3 and dynamic viscosity 0.00345 Pa·s.
    Section 3.3 and A.3; standard physiological average, but neglects non-Newtonian behavior at low shear rates.
  • domain assumption Vessel walls are rigid no-slip boundaries.
    Section 3.3; ignores wall compliance, which can affect hemodynamics.
  • domain assumption Steady-state flow conditions approximate physiological states.
    Section 3.3 and Limitations (Section 6); lacks pulsatile or cardiac-cycle dynamics.
  • domain assumption Inlet cross-section is the largest-area open end of the vascular tree.
    Appendix A.2; morphology-driven inlet selection due to absence of imaging or flow measurements in AneuX.
  • ad hoc to paper Deforming real aneurysm geometries by polygon offset generates physiologically plausible aneurysm evolution shapes.
    Section 3.1 and A.1; no quantitative validation beyond subjective neurosurgeon confirmation, and some shapes reach volume change rates up to 3.5 (Section 3.4).

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Cite this review

Pith. "Pith review of Aneumo: A Large-Scale Multimodal Aneurysm Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks." pith.science (2026). https://pith.science/paper/KO2MZKGK

@misc{pith2026250514717,
  author       = {Pith},
  title        = {Pith review of: Aneumo: A Large-Scale Multimodal Aneurysm Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KO2MZKGK}},
  note         = {Machine review of arXiv:2505.14717}
}
read the original abstract

Intracranial aneurysms (IAs) are serious cerebrovascular lesions found in approximately 5\% of the general population. Their rupture may lead to high mortality. Current methods for assessing IA risk focus on morphological and patient-specific factors, but the hemodynamic influences on IA development and rupture remain unclear. While accurate for hemodynamic studies, conventional computational fluid dynamics (CFD) methods are computationally intensive, hindering their deployment in large-scale or real-time clinical applications. To address this challenge, we curated a large-scale, high-fidelity aneurysm CFD dataset to facilitate the development of efficient machine learning algorithms for such applications. Based on 427 real aneurysm geometries, we synthesized 10,660 3D shapes via controlled deformation to simulate aneurysm evolution. The authenticity of these synthetic shapes was confirmed by neurosurgeons. CFD computations were performed on each shape under eight steady-state mass flow conditions, generating a total of 85,280 blood flow dynamics data covering key parameters. Furthermore, the dataset includes segmentation masks, which can support tasks that use images, point clouds or other multimodal data as input. Additionally, we introduced a benchmark for estimating flow parameters to assess current modeling methods. This dataset aims to advance aneurysm research and promote data-driven approaches in biofluids, biomedical engineering, and clinical risk assessment. The code and dataset are available at: https://github.com/Xigui-Li/Aneumo.

Figures

Figures reproduced from arXiv: 2505.14717 by the authors.

Figure 1
Figure 1. Workflow for deforming patient-specific aneurysm models and simulating vascular hemo [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Simulation results of aneurysm deformation and hemodynamics under different flow rates, [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Schematic illustration of the DeepONet-SwinT model architecture for predicting aneurysm [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (12 more)
Figure 4
Figure 4. Figure 4: Performance comparison of DeepONet and DeepONet-SwinT on the test set. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Comparison of predicted pressure and velocity fields by DeepONet and DeepONet-SwinT [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Visualization of computational mesh structure and boundary conditions for intracranial [PITH_FULL_IMAGE:figures/full_fig_p016_6.png]
Figure 7
Figure 7. Figure 7: Grid independence analysis. The analysis results clearly revealed that as the mesh size was progressively reduced (i.e., mesh resolution increased), the calculated values of the monitored flow field parameters exhibited significant convergence characteristics, indicati…
Figure 8
Figure 8. Figure 8: Residual convergence curve of velocity and pressure. [PITH_FULL_IMAGE:figures/full_fig_p019_8.png]
Figure 9
Figure 9. Figure 9: Comparison of DeepONet and DeepONet-SwinT Training and Verification Convergence. [PITH_FULL_IMAGE:figures/full_fig_p024_9.png]
Figure 10
Figure 10. Figure 10: Effect of Batch Size on Convergence of DeepONet Training and Verification. [PITH_FULL_IMAGE:figures/full_fig_p025_10.png]
Figure 11
Figure 11. Figure 11: Effect of training point density on DeepONet training and validation loss. [PITH_FULL_IMAGE:figures/full_fig_p026_11.png]
Figure 12
Figure 12. Figure 12: Effect of training set flow condition diversity on DeepONet training and validation loss. [PITH_FULL_IMAGE:figures/full_fig_p029_12.png]
Figure 13
Figure 13. Figure 13: Effect of validation set flow condition diversity on DeepONet training and validation loss. [PITH_FULL_IMAGE:figures/full_fig_p030_13.png]
Figure 14
Figure 14. Figure 14: Scaling performance of DeepONet with different training data scales. [PITH_FULL_IMAGE:figures/full_fig_p031_14.png]
Figure 15
Figure 15. Figure 15: Scaling performance of DeepONet-SwinT with different training data scales [PITH_FULL_IMAGE:figures/full_fig_p032_15.png]

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.