Pith. sign in

REVIEW 4 major objections 5 minor 21 references

Two-Stage Generative Model for Intracranial Aneurysm Meshes with Morphological Marker Conditioning

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read AneuG is claimed to be the first deep generative model that jointly models intracranial aneurysm pouches and their parent vessels while allowing conditioning on clinically significant morphological markers.

desk verdict A genuinely novel two-stage IA mesh generator with morphological conditioning, but the controllability evidence is partly circular because the same differentiable marker calculator is used as both loss and test metric. read the letter →

arxiv 2505.10407 v1 pith:6F7VU6QJ submitted 2025-05-15 cs.LG

Wenhao Ding , Choon Hwai Yap , Kangjun Ji , Simão Castro This is my paper · ORCID
classification cs.LG
keywords intracranialaneurysmmeshgenerationvariationalautoencoderconditionalgraphharmonicdeformationmorphologicalmarkersparentvesselmodelinghemodynamics
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 introduces AneuG, a two-stage variational-autoencoder generator for intracranial aneurysm meshes. Its central claim is that, for the first time, one model can generate the aneurysm pouch and its parent vessels together, conditioned on clinically relevant morphological values such as neck width, aspect ratio, lobulation index, and dome volume. This matters because large real-world aneurysm datasets are scarce, and controlled synthetic shape cohorts would allow training of fast blood-flow predictors and systematic studies of how shape features drive hemodynamics. The reported experiments, on 116 middle-cerebral-artery aneurysms, show the model beating a PCA statistical-shape baseline and a diffusion-based generator in fidelity and reconstruction error.

What carries the argument

The load-bearing object is the Graph Harmonic Deformation (GHD) token: scalar coefficients applied to eigenvectors of the canonical mesh's cotangent Laplacian, which warp the canonical mesh into a target aneurysm complex. GHD gives a low-dimensional encoding that preserves node correspondence, so the same mesh can be differentiated through the Morphological Marker Calculator to compute neck width, aspect ratio, lobulation index, and dome volume. The Morphing Energy Alignment term then matches the distribution of rigidity and Laplacian-smoothness energies between real and generated shapes, countering posterior collapse under conditional training. Stage two uses Fourier-mode coefficients as centerline tokens and a tangent-regularization loss so generated vessels connect smoothly to the aneurysm complex.

What would settle it

Take a set of AneuG-generated meshes conditioned on specific marker values, have a neuroradiologist or an independent software package measure neck width, aspect ratio, lobulation index, and dome volume using the standard clinical protocol, and compare those measurements with the requested values; a systematic discrepancy would show that the reported conditioning accuracy reflects the calculator's own conventions rather than clinical morphology.

Watch

Extended reading notes

Core claim

On its own terms, the paper establishes that a two-stage VAE can generate anatomically plausible intracranial aneurysm meshes whose clinically measured shape markers can be set by the user. Stage one encodes the aneurysm complex as Graph Harmonic Deformation tokens and learns a conditional latent space; a differentiable Morphological Marker Calculator feeds marker errors back into training, while Morphing Energy Alignment keeps generated shapes within the population's energy statistics. Stage two generates parent-vessel centrelines as Fourier-mode beams conditioned on the complex, matches tangent directions at the connection, and propagates the cross-section to form a complete mesh. With only 116 training shapes, AneuG reports lower Fréchet and kernel PointNet++ distances, higher diversity, and lower Chamfer distances than PCA and latent-diffusion baselines, and its conditioning accuracy improves when both marker conditioning and energy alignment are active. The authors also demonstrate controlled morphing of a real shape's aspect ratio and lobulation index, and show in flow simulations that higher aspect ratio enlarges low-wall-shear-stress regions while a daughter sac produces more complex flow.

Load-bearing premise

The central assumption is that the differentiable Morphological Marker Calculator's mesh-based measurements of neck width, aspect ratio, lobulation index, and dome volume agree with the clinical measurement protocols these markers come from; conditioning accuracy is computed with the same calculator that enforces the conditioning.

Editorial extensions

If this is right

  • If AneuG's central claim holds, researchers can generate large synthetic aneurysm cohorts with prescribed clinical marker statistics, removing a major data bottleneck for training real-time blood-flow prediction networks.
  • Because the latent code can be fixed while the marker condition is varied, single-marker effects on hemodynamics become directly testable; the paper's own simulations show this for aspect ratio and lobulation.
  • Joint generation of parent vessels produces complete CFD-ready meshes, avoiding the previous practice of merging pouches with idealized or unrelated healthy vessels.
  • The method's reported success with 116 training samples suggests that rare aneurysm locations, where large datasets do not exist, are a feasible target for the same approach.
  • Both proposed training components do measurable work: conditioning accuracy improves with the differentiable marker calculator, and conditional fidelity improves with morphing-energy alignment.

Reading between the lines

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

  • A natural next test, not run in the paper, is to measure markers on AneuG outputs with an independent clinical tool or neuroradiologist; because the same differentiable calculator is used for both conditioning and evaluation, only such an external check can confirm that the controls are clinically meaningful.
  • The GHD representation is template-based, so the same two-stage recipe may transfer to other vessel territories or to organs where a canonical mesh and morphing energies can be defined; the paper does not claim this.
  • The comparison to a diffusion baseline is informative but narrow: the reported gap may reflect data efficiency of VAE plus GHD on 116 samples rather than a general advantage over diffusion generators, and a larger-scale comparison would clarify that.
  • With conditioning applied to a real shape's latent code, the model effectively offers counterfactual aneurysm generation, which could be used to probe rupture-risk markers such as daughter sacs while holding other geometry fixed.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes AneuG, a two-stage VAE-based generative model for 3D intracranial aneurysm (IA) meshes. Stage I encodes the aneurysm complex into Graph Harmonic Deformation (GHD) tokens and trains a conditional VAE with a differentiable Morphological Marker Calculator (MMC) as a conditioning loss plus a Morphing Energy Alignment (MEA) constraint; Stage II generates parent-vessel centrelines from Fourier coefficients conditioned on the generated aneurysm complex. The authors compare unconditional sample quality against PCA and a diffusion baseline using FPD, KPD, TMD, and Chamfer distances, and report conditioning accuracy for aspect ratio, neck width, lobulation index, and dome volume in Table 2. They also provide qualitative demonstrations of conditional morphing and preliminary CFD results.

Significance. If the central claims held, AneuG would be a valuable contribution: source code is promised, GHD tokenization is a sensible low-dimensional representation for meshes with fixed connectivity, and the two-stage design that ties parent vessels to the aneurysm complex addresses a real gap in existing IA shape models. The unconditional results in Table 1 are suggestive, and the application to controlled flow-simulation cohorts is exactly what the community needs. However, the headline controllability claim in Table 2 is currently validated only against the same differentiable marker calculator used in the training objective, and the experimental section does not describe a held-out split or error bars. These omissions make the contribution defensible but not fully established; the architecture and the unconditional-fidelity evidence are the strongest parts.

major comments (4)
  1. [Section 2.3 and Table 2] The conditioning-accuracy metric is circular. The differentiable Morphological Marker Calculator (MMC) computes NW, AR, LI, and V and is minimized as Lcond during stage-I training; the same MMC is then used to compute the CA rows in Table 2. Because the evaluation metric is the same differentiable proxy as the training objective, the reported CA values (1.80-12.91%) demonstrate that the generator matches the calculator, not that a clinician or an independent measurement protocol would recover the requested marker values. This directly targets the paper's central controllability claim. The authors should validate MMC against clinical or radiological measurements, or against manual annotations on a held-out set, and report CA with an independent measurement protocol.
  2. [Section 3, Tables 1 and 2] No train/validation/test split is described, and all reported numbers appear to be computed on the same 116-case cohort used for training. Without a held-out split or cross-validation, the reconstruction and fidelity metrics can be optimistically biased, and the conditioning numbers cannot be interpreted as generalization performance. Please report a fixed split (or repeated cross-validation) and per-seed standard deviations, especially since the dataset is small and the VAE is trained on the same cases whose distributions are evaluated in Tables 1 and 2.
  3. [Section 2.3] The lobulation index (LI) is defined as dome surface area divided by dome volume. This quantity has units of inverse length and is not a fraction, nor is it a recognized clinical descriptor of lobulation; the paper does not show that it correlates with the presence of a daughter sac or with established lobulation features used in the clinical literature. Since Table 2 and the qualitative 'daughter sac' demonstration in Fig. 2f rely on this marker, the authors should either adopt a standard lobulation measure (e.g., number or depth of blebs) and validate it, or clearly reposition LI as a purely geometric summary and temper the clinical claims made through it.
  4. [Section 2.4 and Fig. 2] The claim that AneuG 'simultaneously models the aneurysm complex and its parent vessels' is not quantitatively evaluated. Table 1 reports only metrics on the aneurysm-complex shape (FPD/KPD/TMD/CDv/CDn), and there is no evaluation of generated centreline fidelity, parent-vessel radius profiles, or the realism of the merged two-vessel mesh. As written, the second-stage contribution rests on a single qualitative figure. Please add quantitative vessel-level metrics or explicitly restrict the validation claim to the aneurysm complex.
minor comments (5)
  1. [Section 3, Dataset] There is a typo: 'the the largest publicly available IA dataset' should read 'the largest publicly available IA dataset'.
  2. [Equation (2)] The notation Rk(vk) is garbled in the text, and the roles of the rotation matrix, the sine basis functions, and the branch index k should be spelled out more clearly.
  3. [Section 2.2] The GHD mode truncation n and the number of Fourier modes are free parameters that affect the reported reconstruction numbers; please state their values in the main text or appendix.
  4. [Table 2] The check-mark columns for MEA and MMC are ambiguous across the different condition groups; label each configuration explicitly, for example 'MEA on/off' and 'MMC on/off' under each marker group.
  5. [References] Some reference entries use 'et al.' without full author lists, and reference [19] appears unused in the text; please check citation usage and complete the bibliographic details.

Circularity Check

1 steps flagged · score 6.0 of 10

Conditioning accuracy is circular: the same Morphological Marker Calculator used as the training loss Lcond is also used to compute CA in Table 2, so the reported controllability is self-consistency with a differentiable proxy rather than independent clinical validation.

  1. fitted input called prediction [Section 2.3 (Morphological Marker Calculator) and Section 3 (Metrics; Table 2)]
    "During training, we randomly sample from the latent space and pass the generated shapes through a differentiable Morphological Marker Calculator (MMC) to compute the MSE accuracy of morphology parameters in generated shapes (Lcond). ... For conditioning accuracy, We use the relative L2 error on recalled conditions."

    The conditioning accuracy (CA) in Table 2 is the relative L2 error between the requested morphological marker values and the values measured by the MMC on generated meshes. The same MMC is exactly the differentiable function minimized during stage-I training through Lcond. Therefore CA measures how well the generator matches the proxy it was explicitly optimized against, not whether the meshes have clinically or radiologically verified neck width, aspect ratio, lobulation index, or dome volume.

full rationale

The main circularity is confined to the conditional-marker claim. The same differentiable MMC is used as the training loss Lcond (Section 2.3) and as the evaluation metric for conditioning accuracy (Section 3, Table 2), so the reported CA values do not provide independent evidence that generated shapes realize clinically meaningful morphological measurements. The GHD encoding is adopted from prior work [16] with overlapping authorship, but its benefit is supported by external comparisons against PCA and diffusion baselines in Table 1, so that self-citation is not load-bearing here. The fidelity metrics (FPD, KPD, TMD) and reconstruction Chamfer distances are based on pretrained or extrinsic measures and are not circular. Because the paper's headline capability of controllable generation is directly supported by the circular metric, the score reflects partial circularity rather than wholesale collapse.

Assumptions & free parameters 5 free parameters · 5 assumptions · 1 invented entities

The central method depends on several untested domain assumptions: the GHD representation basis, the normality of morphing energies, the fidelity of the unconditional VAE as a prior, the clinical equivalence of MMC, and the sine-beam model for vessels. These are not derived from first principles and are not independently validated in the paper.

free parameters (5)
  • GHD mode truncation n
    Number of graph Laplacian eigenvectors used for the GHD token representation; chosen by hand, no value or sensitivity analysis given (Section 2.2).
  • Vessel Fourier mode count
    Number of sine modes used to encode parent vessel centerlines; chosen by hand, no value or ablation reported (Section 2.2).
  • VAE latent dimension
    Dimension of latent z in both stage VAEs; chosen by hand, not reported in text.
  • MEA loss weight
    Weight of LMEA in stage I training; chosen by hand, no value or sensitivity analysis given (Section 2.3).
  • MMC conditioning loss weight
    Weight of Lcond from differentiable Morphological Marker Calculator; chosen by hand, no value reported (Section 2.3).
assumptions (5)
  • domain assumption GHD tokens with n eigenvectors can faithfully reconstruct realistic IA complexes
    Section 2.2 relies on [16] GHD morphing; if the basis cannot represent relevant shape variations, both reconstruction and generation are biased.
  • domain assumption Morphing energies Er and El are normally distributed in the IA population
    Section 2.3 performs Shapiro-Wilk on 116 samples and then uses Gaussian KL divergence in LMEA; a non-normal distribution would make the alignment loss mis-specified.
  • domain assumption Samples from the unconditional stage I VAE approximate the true shape distribution for estimating conditional marker combinations
    Section 2.3 uses synthetic shapes from a pre-trained unconditional VAE to avoid unphysiological condition combinations; if the unconditional model is biased, the conditioning prior is biased.
  • domain assumption The differentiable Morphological Marker Calculator produces values equivalent to clinical measurements
    Section 2.3 defines NW, AR, LI, V through mesh operations; Table 2 uses the same MMC for conditioning accuracy, so clinical equivalence is assumed, not shown.
  • domain assumption Parent vessel centerlines are well approximated by warped beams with sine mode shapes
    Section 2.2 Eq. 2-3 models each branch as a warped beam; complex vessel tortuosity outside this basis would not be captured.
invented entities (1)
  • Lobulation index (LI)
    purpose: New morphological marker defined as dome surface area divided by dome volume, used for conditioning in Table 2.
    Defined in Section 2.3 but not validated against clinical imaging measurements or existing lobulation scoring; its clinical validity is assumed.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Two-Stage Generative Model for Intracranial Aneurysm Meshes with Morphological Marker Conditioning." pith.science (2026). https://pith.science/paper/6F7VU6QJ

@misc{pith2026250510407,
  author       = {Pith},
  title        = {Pith review of: Two-Stage Generative Model for Intracranial Aneurysm Meshes with Morphological Marker Conditioning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6F7VU6QJ}},
  note         = {Machine review of arXiv:2505.10407}
}
read the original abstract

A generative model for the mesh geometry of intracranial aneurysms (IA) is crucial for training networks to predict blood flow forces in real time, which is a key factor affecting disease progression. This need is necessitated by the absence of a large IA image datasets. Existing shape generation methods struggle to capture realistic IA features and ignore the relationship between IA pouches and parent vessels, limiting physiological realism and their generation cannot be controlled to have specific morphological measurements. We propose AneuG, a two-stage Variational Autoencoder (VAE)-based IA mesh generator. In the first stage, AneuG generates low-dimensional Graph Harmonic Deformation (GHD) tokens to encode and reconstruct aneurysm pouch shapes, constrained to morphing energy statistics truths. GHD enables more accurate shape encoding than alternatives. In the second stage, AneuG generates parent vessels conditioned on GHD tokens, by generating vascular centreline and propagating the cross-section. AneuG's IA shape generation can further be conditioned to have specific clinically relevant morphological measurements. This is useful for studies to understand shape variations represented by clinical measurements, and for flow simulation studies to understand effects of specific clinical shape parameters on fluid dynamics. Source code and implementation details are available at https://github.com/anonymousaneug/AneuG.

Figures

Figures reproduced from arXiv: 2505.10407 by the authors.

Figure 1
Figure 1. Overview of AneuG. To address these unmet needs, we propose a two-stage conditional generation model, AneuG. In the first stage, the model generates the IA pouch and regions immediately surrounding it — referred to as the aneurysm complex—conditional [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Generation gallery. Rotational video for c and d are provided in supplementary material [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

21 extracted references · 18 canonical work pages

  1. [1]

    Neurosurgical Review45(1), 1–25 (2022)

    Lee, K.S., Zhang, J.J.Y., Nguyen, V., Han, J., Johnson, J.N., Kirollos, R., Teo, M.: The Evolution of Intracranial Aneurysm Treatment Techniques and Future Direc- tions. Neurosurgical Review45(1), 1–25 (2022)

    work page 2022

  2. [2]

    Neurosurgery 82(4), 491–496 (2018)

    Mocco, J., Brown, R.D., Torner, J.C., Capuano, A.W., Fargen, K.M., Raghavan, M.L., Piepgras, D.G., Meissner, I., Huston, J.: Aneurysm Morphology and Pre- diction of Rupture: An International Study of Unruptured Intracranial Aneurysms Analysis. Neurosurgery 82(4), 491–496 (2018)

    work page 2018

  3. [3]

    Scientific Reports 7(1), 5331 (2017)

    Zhou, G., Zhu, Y., Yin, Y., Su, M., Li, M.: Association of Wall Shear Stress with Intracranial Aneurysm Rupture: Systematic Review and Meta-Analysis. Scientific Reports 7(1), 5331 (2017). doi: 10.1038/s41598-017-05886-w. Erratum in: Scientific Reports 8(1), 5244 (2018)

    work page doi:10.1038/s41598-017-05886-w 2017

  4. [4]

    et al.: Prediction of 3D Cardiovascular Hemodynamics Before and After Coronary Artery Bypass Surgery via Deep Learning

    Li, G., Wang, H., Zhang, M. et al.: Prediction of 3D Cardiovascular Hemodynamics Before and After Coronary Artery Bypass Surgery via Deep Learning. Communi- cations Biology 4, 99 (2021)

    work page 2021

  5. [5]

    Surgical Neurology International 12, 506 (2021)

    Merritt, W.C., Berns, H.F., Ducruet, A.F., Becker, T.A.: Definitions of intracra- nial aneurysm size and morphology: A call for standardization. Surgical Neurology International 12, 506 (2021)

    work page 2021

  6. [6]

    Frontiers in Bioengineering and Biotechnology12 (2024)

    Goetz, A., Jeken-Rico, P., Pelissier, U., Chau, Y., Sédat, J., Hachem, E.: AnXplore: a comprehensive fluid-structure interaction study of 101 intracranial aneurysms. Frontiers in Bioengineering and Biotechnology12 (2024)

    work page 2024

  7. [7]

    Building a Synthetic Vascular Model: Evaluation in an Intracranial Aneurysms Detection Scenario

    Nader, R., Autrusseau, F.: Building a Synthetic Vascular Model: Evaluation in an Intracranial Aneurysms Detection Scenario. arXiv preprint arXiv:2411.02477 (2024)

    work page Pith review arXiv 2024

  8. [8]

    Aneumo: A Large-Scale Comprehensive Synthetic Dataset of Aneurysm Hemodynamics

    Li, X., Zhou, Y., Xiao, F., Guo, X., Zhang, Y., Jiang, C., Ge, J., Wang, X., Wang, Q., Zhang, T., Lin, C., Cheng, Y., Qi, Y.: Aneumo: A Large-Scale Comprehensive Synthetic Dataset of Aneurysm Hemodynamics. arXiv preprint arXiv:2501.09980 (2025)

    work page Pith review arXiv 2025

Show all 21 references
  1. [9]

    et al.: Wall Enhancement, Hemodynamics, and Mor- phology in Unruptured Intracranial Aneurysms with High Rupture Risk

    Lv, N., Karmonik, C., Chen, S. et al.: Wall Enhancement, Hemodynamics, and Mor- phology in Unruptured Intracranial Aneurysms with High Rupture Risk. Transla- tional Stroke Research11, 882–889 (2020)

    2020

  2. [10]

    et al.: Association between hemodynamics, morphology, and rupture risk of intracranial aneurysms: a computational fluid modeling study

    Qiu, T., Jin, G., Xing, H. et al.: Association between hemodynamics, morphology, and rupture risk of intracranial aneurysms: a computational fluid modeling study. Neurological Sciences 38, 1009–1018 (2017)

    2017

  3. [11]

    Journal of Magnetic Resonance Imaging39(1), 120–131 (2014)

    Schnell, S., Ansari, S.A., Vakil, P., Wasielewski, M., Carr, M.L., Hurley, M.C., Bendok, B.R., Batjer, H., Carroll, T.J., Carr, J., Markl, M.: Three-dimensional hemodynamics in intracranial aneurysms: influence of size and morphology. Journal of Magnetic Resonance Imaging39(1)...

    2014

  4. [12]

    Frontiers in Physiology12, 733444 (2021)

    Li, G., Song, X., Wang, H., Liu, S., Ji, J., Guo, Y., Qiao, A., Liu, Y., Wang, X.: Prediction of Cerebral Aneurysm Hemodynamics With Porous-Medium Models of Flow-Diverting Stents via Deep Learning. Frontiers in Physiology12, 733444 (2021)

    2021

  5. [13]

    arXiv preprint arXiv:2301.11445 (2023)

    Zhang, B., Tang, J., Niessner, M., Wonka, P.: 3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models. arXiv preprint arXiv:2301.11445 (2023). 10 W. Ding et al

    2023 arXiv

  6. [14]

    Ap- plied Sciences 12(1), 394 (2022)

    Catalano, C., Agnese, V., Gentile, G., Raffa, G.M., Pilato, M., Pasta, S.: Atlas- Based Evaluation of Hemodynamic in Ascending Thoracic Aortic Aneurysms. Ap- plied Sciences 12(1), 394 (2022)

    2022

  7. [15]

    In: Advances in Neural Information Processing Systems 36, pp

    Zhao, Z., Liu, W., Chen, X., Zeng, X., Wang, R., Cheng, P., Fu, B., Chen, T., Yu, G., Gao, S.: Michelangelo: Conditional 3D Shape Generation based on Shape-Image- Text Aligned Latent Representation. In: Advances in Neural Information Processing Systems 36, pp. 73969–73982. Cur...

    2023

  8. [16]

    arXiv preprint arXiv:2409.02070v2 (2024)

    Luo, Y., Sesija, D., Wang, F., Wu, Y., Ding, W., Huang, J., et al.: Explicit Dif- ferentiable Slicing and Global Deformation for Cardiac Mesh Reconstruction. arXiv preprint arXiv:2409.02070v2 (2024)

    2024 arXiv

  9. [17]

    In: Belyaev, A., Garland, M

    Sorkine, O., Alexa, M.: As-Rigid-As-Possible Surface Modeling. In: Belyaev, A., Garland, M. (eds.) Geometry Processing, pp. 109–116. The Eurographics Associa- tion (2007)

    2007

  10. [18]

    In: Proceedings of the 26th Annual Con- ference on Computer Graphics and Interactive Techniques (SIGGRAPH ’99), pp

    Desbrun, M., Meyer, M., Schröder, P., Barr, A.H.: Implicit fairing of irregular meshes using diffusion and curvature flow. In: Proceedings of the 26th Annual Con- ference on Computer Graphics and Interactive Techniques (SIGGRAPH ’99), pp. 317–324. ACM Press/Addison-Wesley Publ...

    1999

  11. [19]

    arXiv preprint arXiv:1609.02907 (2017).https://arxiv.org/abs/1609

    Kipf, T.N., Welling, M.: Semi-Supervised Classification with Graph Convolutional Networks. arXiv preprint arXiv:1609.02907 (2017).https://arxiv.org/abs/1609. 02907

    2017 arXiv

  12. [20]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp

    Zhou, L., Du, Y., Wu, J.: 3D Shape Generation and Completion Through Point- Voxel Diffusion. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 5826–5835. IEEE (2021)

    2021

  13. [21]

    Neurology85(10), 881– 889 (2015)

    Etminan, N., Brown, R.D., Jr., Beseoglu, K., et al.: The Unruptured Intracranial Aneurysm Treatment Score: A Multidisciplinary Consensus. Neurology85(10), 881– 889 (2015)

    2015

Pith tools

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