Hierarchical Part-based Generative Model for Realistic 3D Blood Vessel

Bingzhi Shen; Caixia Dong; Guoqing Zhang; Jiahao Lai; Sihong Zhang; Siqi Chen; Xuejin Chen; Yang Li

REVIEW 4 major objections 5 minor 31 references

Hierarchical Part-based Generative Model for Realistic 3D Blood Vessel

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A hierarchical part-based framework that separates global tree topology from local segment geometry yields more topologically consistent 3D blood vessel networks than existing generative methods.

desk verdict Novel part-based vessel generator, but the spanning-tree preprocessing hides the Circle of Willis ring and the realism claim is overstated. read the letter →

arxiv 2507.15223 v1 pith:TQ4R5PRE submitted 2025-07-21 cs.CV

Siqi Chen , Guoqing Zhang , Jiahao Lai , Bingzhi Shen , Sihong Zhang , Caixia Dong , Xuejin Chen , Yang Li This is my paper · ORCID

classification cs.CV

keywords vasculaturevesselgeneration3Dshapemodelinghierarchicalstructurepart-basedmethodrecursivevariationalautoencodertransformerVAEkeygraph

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

Blood vessel networks are hard to generate because they are globally tree-like and locally curved and irregular. This paper argues that existing point-cloud and implicit-field generative models blur those two levels, so they produce disconnected or anatomically odd vessels, especially on complex real-world coronary data. To fix that, the authors split the problem: one stage generates the branching skeleton as a binary tree (the 'key graph'), another generates each vessel segment as a 3D curve from a geometric descriptor, and a final stage assembles segments onto the graph. They report that this three-stage part-based approach beats the baselines on graph-based measures of structural fidelity and topological consistency, while remaining competitive on point-cloud metrics. If correct, it establishes a new application area for part-based generative modeling: vascular anatomy where topology matters.

What carries the argument

The key graph is the central object: a binary tree whose nodes carry spatial coordinates, a direction vector, and the geometric descriptor $C=[\ell,\delta,\kappa,\rho]$, with each edge corresponding to one vessel segment. The encoding and decoding machinery is a recursive variational autoencoder (RVAE) that aggregates child hidden states into parents with an MLP, classifies node presence during decoding, and is regularized by a KL term; segments are generated by a Transformer VAE whose tokens are skeleton points $[x,y,z,r]$ and whose condition is $C$. Assembly walks the decoded graph depth-first, attaching each generated segment by scaling, translation, and two rotation steps so that segment direction matches the node's orientation, then reconstructs the surface from the skeleton using the per-point radii.

What would settle it

Run the method on a vascular dataset that contains anatomically known loops or trifurcations (for example, the circle of Willis with its communicating arteries); if the extracted maximum spanning tree removes those cycles and the generated samples never contain a loop or triplet junction, then the claim that the model preserves topological consistency is refuted.

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Extended reading notes

Core claim

The central claim is that a vascular network is better generated as a composition of parts than as a whole shape: first a binary-tree 'key graph' that fixes the global branching topology, then independent vessel segments generated as ordered 3D point sequences, then an assembly stage that places each segment onto the graph by scaling, translation, and rotation. The authors represent each key-graph node by its 3D coordinate, the local direction $[n_x,n_y,n_z]$, and a geometric descriptor $C=[\ell,\delta,\kappa,\rho]$ (length, endpoint distance, curvature, tree depth), and train a recursive variational autoencoder on this tree structure while a Transformer-based variational autoencoder generates each segment conditioned on $C$. On ImageCAS coronary arteries, VascuSynth synthetic trees, and processed Circle-of-Willis meshes, the method claims the best graph-based metrics (degree and Laplacian-spectrum discrepancy, graph Wasserstein distance), which are the metrics that score topological consistency; on point-cloud metrics it is competitive but not always best.

Load-bearing premise

The load-bearing premise is that a real vascular network can be faithfully represented as a binary tree; the preprocessing builds a maximum spanning tree, so any genuine loop or multi-way junction that the spanning tree discards is absent from every generated vessel.

Editorial extensions

If this is right

Generated vessels preserve the binary branching hierarchy better than point-cloud and implicit neural-field baselines, as measured by degree and Laplacian-spectrum discrepancies.
The part-based decomposition allows complex real-world datasets like ImageCAS to be modeled without the topological breakdowns seen in baseline reconstructions.
Because local segments are generated independently and assembled, the framework can scale to vessels with many bifurcations without the fidelity degradation VesselVAE suffers.
Skeleton-based assembly with predicted radii yields tubular meshes that avoid the holes point-cloud reconstructions produce.
This is the first part-based generative approach for 3D vascular modeling, opening that direction for future work.

Reading between the lines

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

If the decomposition generalizes, the same key-graph-plus-segments recipe could be applied to other tree-like anatomical structures such as airways, bile ducts, or plant roots, where global topology matters as much as local shape.
The reliance on a maximum spanning tree means the method will silently drop vascular cycles or trifurcations; a natural test is to quantify how much topology is lost in preprocessing and whether the generative model can ever reproduce it.
One could swap the RVAE for a different hierarchical generator or the Transformer VAE for a latent diffusion per segment; the paper's contribution is the decomposition, not the specific architectures, so those swaps are plausible extensions.

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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

Two things to know about arXiv:2507.15223. First, the core idea is genuinely new: nobody has modeled 3D vessels as a combination of a global binary-tree key graph and locally generated tubular segments. That decomposition is clean, the three-stage pipeline (RVAE for topology, transformer VAE for segments, assembly) is well-specified, and the code is public. Second, the paper's claim to 'realistic' generation on the Circle of Willis dataset is undermined by its own preprocessing. Section 4 says it builds a maximum spanning tree from the skeleton. CoW contains an anastomotic ring (posterior communicating arteries); a spanning tree must delete an edge, so the model can never produce that cycle. The graph metrics in Table 1 are then computed on the generated trees and on baseline skeletons that likely went through the same spanning-tree preprocessing, so the missing ring is invisible to every metric. That is not just a performance gap; it changes the claim from 'topological consistency' to 'tree-consistency.' The paper never acknowledges this limitation.

Where the paper is solid: for tree-like vessels—coronary arteries, VascuSynth—the part-based approach makes sense and the graph metrics (degree MMD, spectrum, GWD) show clear gains over VesselVAE and TreeDiffusion. The separation of global topology from local geometry is well-motivated, and the qualitative results look structurally coherent.

Soft spots, in order of worry. (1) The CoW cycle problem above is load-bearing for the realism claim on that dataset. (2) The paper cites Prabhakar et al. [15] but does not compare against it, even though it is the most relevant vessel-graph diffusion baseline. (3) No error bars anywhere; given the small test sets, some of the metric differences may not be significant. (4) The abstract says 'superior performance over existing methods,' but Table 1 shows PointDiffusion wins Chamfer distance by an order of magnitude on all datasets, and TreeDiffusion beats the method on spectrum MMD on VascuSynth. That is overstatement.

Bottom line: worth a serious referee. The idea is a useful addition to the vessel-generation toolbox, provided the CoW limitation is addressed or the claims are narrowed to acyclic vasculature. I would send it to review with a request for clarification.

Referee Report

4 major / 5 minor

Summary. The paper proposes a three-stage hierarchical part-based generative model for 3D blood vessel networks. Stage 1 uses a recursive variational autoencoder to generate a binary-tree ``key graph'' from skeleton bifurcation and terminal points. Stage 2 uses a transformer-based variational autoencoder to synthesize vessel segments conditioned on a geometric descriptor (length, straight-line distance, curvature, and depth). Stage 3 assembles the generated segments into a complete vessel skeleton according to the key graph and reconstructs the final mesh. The method is evaluated on ImageCAS, VascuSynth, and the Circle of Willis (CoW) datasets against VesselVAE, PointDiffusion, and TreeDiffusion, using point-based metrics (JSD, Chamfer distance) and graph-based metrics (degree MMD, Laplacian spectrum MMD, Graph Wasserstein distance). The authors report leading results on most graph metrics but substantially worse Chamfer distances than PointDiffusion, and they claim this is the first successful part-based generative approach for 3D vessel modeling.

Significance. If the underlying tree representation is accepted, the hierarchical part-based decomposition is a plausible and potentially useful contribution: it explicitly separates global branching topology from local segment geometry, and the three-stage pipeline is clearly described. The release of code and the use of public datasets support reproducibility. However, the central claim of ``realistic 3D blood vessel'' generation and ``topological consistency'' is weakened by the maximum-spanning-tree preprocessing, which discards cyclic topology present in the CoW dataset and forces all generated structures to be binary trees. The quantitative support is also mixed: the method trails PointDiffusion by a large margin on Chamfer distance on every dataset, so the headline ``superior performance'' statement is stronger than the evidence. With a clearly scoped claim (tree-shaped vascular networks) and a clarified evaluation pipeline, the work would be a useful addition; as written, the broad realism and topology claims need revision.

major comments (4)

[§4 Data Preparation] The paper's central claim of topological consistency is compromised by the maximum spanning tree preprocessing. In §4, the authors state that they ``adopt an adaptive mapping [30] method to construct a key graph and build a maximum spanning tree.'' For the Circle of Willis (CoW) dataset, the ground-truth vascular network contains an anastomotic ring (the posterior communicating arteries); a maximum spanning tree of a cyclic graph necessarily deletes at least one edge. Stage 1 then generates only binary trees, and Stage 3 assembles a tree. Consequently, the generated vessels cannot reproduce the cyclic topology of CoW, and the graph metrics in Table 1 (Deg., Spec., GWD) compare tree representations rather than the true vascular topology. The claim that the model achieves ``topological consistency'' is therefore only valid within a tree approximation. Please either (a) restrict the claims and method to tree-shaped vascular networks and document the fraction of test cases that are non-tree, or (b) add a cycle-aware evaluation (e.g., Betti number or cycle count computed on original skeletons) and a baseline comparison that does not rely on MST reduction.
[§3 Stage 1; §4 Data Preparation] The binary-tree representation is underspecified for non-binary branch points. The decoder in Eqs. (2)--(3) uses NodeCLS to predict whether left and/or right children exist, so any node with more than two children (trifurcations or higher multifurcations) must be split into multiple binary nodes. The paper does not describe how such splits are performed, how the geometric descriptor C (length, distance, curvature, depth) is assigned to the resulting sub-segments, or how these artificial splits affect the assembled skeleton. Since real vascular networks can contain such branch points, this missing specification affects the generality of the method and the interpretation of the reported graph metrics.
[§4 Table 1] The statement that the model ``achieves competitive performance across most tasks'' should be qualified by the large Chamfer distance gap. On CD, the proposed method is worse than PointDiffusion by a factor of roughly 20 on ImageCAS (24.4 vs 1.1), about 5 on VascuSynth (34.8 vs 7.0), and about 15 on CoW (15.1 vs 1.0). Since CD is a standard geometric reconstruction metric, the paper needs to explain why the graph metrics should be prioritized, and the ``superior performance'' claim in the abstract and conclusion is too strong as written.
[§4 Metrics] The computation of the graph-based metrics is not fully specified. It is unclear whether the skeletons of all methods (including PointDiffusion's point clouds and TreeDiffusion's implicit fields) are derived with the same skeletonization and maximum spanning tree preprocessing as the proposed method, or whether the ground-truth graphs are also reduced to trees before computing Deg., Spec., and GWD. This is essential for a fair comparison and for assessing whether the reported improvements in topological consistency come from the generative model itself or from a preprocessing step applied uniformly to all methods. Please provide the precise preprocessing pipeline for each baseline and the details of the metric computation.

minor comments (5)

[Abstract; Table 1; Implementation Details] There are several typographical issues: ``frame work'' in the abstract, ``T able 1'' in the table caption, and ``Instage 1'' in the Implementation Details section.
[Figure 5] The caption states that the comparison uses ``TreeDiffusion's best-performing samples,'' but the criterion for selecting these samples is not defined; please state the selection procedure to avoid the appearance of cherry-picking.
[Abstract; Contributions] The claim ``first successful application of a part-based generative approach'' is stronger than the evidence; consider softening it to ``a first part-based approach'' given the mixed quantitative results and the tree-only scope.
[Related Work] The related work section introduces reference [15] only in a list of baselines; a brief discussion of how the proposed key-graph representation differs from the graph-generation approach in [15] would help position the contribution.
[Introduction] The sentence ``We are the first study to address this challenging dataset'' for ImageCAS is vague; please clarify whether this refers to generative modeling of vessels, segmentation, or something else, and verify the claim against the literature.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the three-stage generative pipeline is trained with standard VAE losses and evaluated on held-out public data; the maximum-spanning-tree preprocessing is a representation limitation, not a self-referential prediction.

full rationale

The derivation is self-contained. Stage 1 trains a recursive VAE on maximum-spanning-tree key graphs extracted from ground-truth skeletons; Stage 2 trains a Transformer VAE on segment curves conditioned on geometric descriptors C; Stage 3 assembles generated segments according to the generated key graph. The objectives (Eqs. 4 and 5) are standard reconstruction, cross-entropy, and KL terms, and no fitted parameter is later renamed as a prediction: all reported metrics compare generated samples with held-out data from ImageCAS, VascuSynth, and CoW. The self-citations ([29], [30]) provide preprocessing and mesh-reconstruction tools; these are externally falsifiable algorithms and do not supply the paper's central claim. The maximum-spanning-tree preprocessing is the main validity concern: for the Circle of Willis it deletes any non-tree cycle, so graph metrics (Deg., Spec., GWD) can only assess tree-representable topology, and the paper does not state this limitation. However, this is an external-validity gap, not circularity, because the model's output is not equal to its input by construction and the learned branching structure is still data-driven and evaluated against preprocessed ground truth rather than against the model's own fitted values. No circular step can be exhibited from the paper's equations or citations.

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

The central claim relies on domain assumptions about vascular representation and conditioning; no free parameters or invented physical entities are introduced beyond standard network hyperparameters.

assumptions (3)

domain assumption Real vascular networks are adequately represented as binary trees via a maximum spanning tree built from skeleton bifurcation and terminal points.
Invoked in Section 4 Data Preparation ('build a maximum spanning tree'); this discards any cycles or multifurcations present in real vessels, which could make generated anatomy incomplete.
domain assumption The geometric descriptor C = [length, straight-line distance, curvature, depth] is sufficient to condition realistic vessel segment generation.
Stage 2 uses C as the only conditioning input for the transformer VAE; the paper does not justify why these four scalars capture all relevant segment geometry.
domain assumption Skeleton and radius information extracted via morphological operations and adaptive mapping [30] is accurate enough for graph construction and assembly.
All downstream stages depend on this preprocessing; errors in skeleton extraction would propagate into both the key graph and segment geometry.

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Cite this review

Pith. "Pith review of Hierarchical Part-based Generative Model for Realistic 3D Blood Vessel." pith.science (2026). https://pith.science/paper/TQ4R5PRE

@misc{pith2026250715223,
  author       = {Pith},
  title        = {Pith review of: Hierarchical Part-based Generative Model for Realistic 3D Blood Vessel},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TQ4R5PRE}},
  note         = {Machine review of arXiv:2507.15223}
}

read the original abstract

Advancements in 3D vision have increased the impact of blood vessel modeling on medical applications. However, accurately representing the complex geometry and topology of blood vessels remains a challenge due to their intricate branching patterns, curvatures, and irregular shapes. In this study, we propose a hierarchical part-based frame work for 3D vessel generation that separates the global binary tree-like topology from local geometric details. Our approach proceeds in three stages: (1) key graph generation to model the overall hierarchical struc ture, (2) vessel segment generation conditioned on geometric properties, and (3) hierarchical vessel assembly by integrating the local segments according to the global key graph. We validate our framework on real world datasets, demonstrating superior performance over existing methods in modeling complex vascular networks. This work marks the first successful application of a part-based generative approach for 3D vessel modeling, setting a new benchmark for vascular data generation. The code is available at: https://github.com/CybercatChen/PartVessel.git.

Figures

Figures reproduced from arXiv: 2507.15223 by the authors.

**Figure 1.** (a) Visualization of a Real-world Coronary Artery Dataset. The vascular network displays a hierarchical, tree-like structure, with localized curvatures and complex branching patterns. (b) The histograms of vessel length and number of bifurcations for four different datasets. such as preoperative simulations [14] and medical image analysis [31], facilitating more accurate and effective decision-making. Moreover, th… view at source ↗

**Figure 2.** Overall pipeline of our method. Stage 1. Key Graph Generation: learn a global hierarchical tree. Stage 2. Vessel Segment Generation: model local 3D curve based on geometric conditions. Stage 3. Hierarchical Vessel Assembly: reconstruct the vessel skeleton by assembling segments based on the global layout. as vessel modeling by [6]. To extend RAE into a generative framework, we employ a Recursive Variational Autoenc… view at source ↗

**Figure 3.** (a) The encoding and decoding process of the model in Stage 1. (b) The two types of rotation processes in Stage 3. a KL divergence to regularize the latent space. These terms together define the final objective: Loss = MSE(ˆv, v) + CrossEntropy(yˆ, y) + DKL(q(zroot)∥p(zroot)). (4) Stage 2. Vessel Segment Generation. For each vessel segment identified in the key graph, we represent the skeleton as an ordered sequence… view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Reconstruction result from three different methods on ImageCAS dataset. Our approach produces more robust and anatomically consistent results compared to point cloud-based and INR-based methods. son models failed to successfully generate the basic morphology of the ves…

**Figure 5.** Figure 5: Examples of generation results from TreeDiffusion and our model on CoW, VascuSynth, and ImageCAS datasets (from top to bottom) [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

31 extracted references · 25 canonical work pages

[15]

In: Interna- tional Conference on Medical Image Computing and Computer-Assisted Interven- tion

Prabhakar, C., Shit, S., Musio, F., Yang, K., Amiranashvili, T., Paetzold, J.C., Li, H.B., Menze, B.: 3d vessel graph generation using denoising diffusion. In: Interna- tional Conference on Medical Image Computing and Computer-Assisted Interven- tion. pp. 3–13. Springer (2024)

work page 2024
[30]

In: International Symposium on Bioinformatics Research and Applications

Zhang, G., Li, Y.: A geometric algorithm for blood vessel reconstruction from skeletal representation. In: International Symposium on Bioinformatics Research and Applications. pp. 114–126. Springer (2024)

work page 2024
[1]

In: Proceedings of the 35th International Conference on Machine Learning (ICML)

Achlioptas, P., Diamanti, O., Mitliagkas, I., Guibas, L.J.: Learning representations and generative models for 3d point clouds. In: Proceedings of the 35th International Conference on Machine Learning (ICML). pp. 40–49. PMLR (2018)

work page 2018
[2]

arXiv preprint arXiv:1910.14388 (2019)

Belli,D.,Kipf,T.:Image-conditionedgraphgenerationforroadnetworkextraction. arXiv preprint arXiv:1910.14388 (2019)

arXiv 2019
[3]

arXiv preprint arXiv:1512.03012 (2015)

Chang, A.X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., et al.: Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012 (2015)

arXiv 2015
[4]

In: Computer graphics forum

Chaudhuri, S., Ritchie, D., Wu, J., Xu, K., Zhang, H.: Learning generative models of 3d structures. In: Computer graphics forum. vol. 39, pp. 643–666. Wiley Online Library (2020)

work page 2020
[5]

Dong, C., Xu, S., Dai, D., Zhang, Y., Zhang, C., Li, Z.: A novel multi-attention, multi-scale3ddeepnetworkforcoronaryarterysegmentation.MedicalImageAnal- ysis 85, 102745 (2023)

work page 2023
[6]

In: International Con- ference on Medical Image Computing and Computer-Assisted Intervention

Feldman, P., Fainstein, M., Siless, V., Delrieux, C., Iarussi, E.: Vesselvae: Recur- sive variational autoencoders for 3d blood vessel synthesis. In: International Con- ference on Medical Image Computing and Computer-Assisted Intervention. pp. 67–76. Springer (2023)

work page 2023

Show all 31 references

[7]

Computerized Medical Imaging and Graphics34(8), 605–616 (2010)

Hamarneh,G.,etal.:Vascusynth:Simulatingvasculartreesforgeneratingvolumet- ric image data with ground-truth segmentation and tree analysis. Computerized Medical Imaging and Graphics34(8), 605–616 (2010)

2010
[8]

American journal of neuroradiology 23(7), 1199– 1205 (2002) 10 S

Hochmuth, A., Spetzger, U., Schumacher, M.: Comparison of three-dimensional rotational angiography with digital subtraction angiography in the assessment of ruptured cerebral aneurysms. American journal of neuroradiology 23(7), 1199– 1205 (2002) 10 S. Chen et al

2002
[9]

In: Proceedings of the AAAI conference on artificial intelligence

Li, J., Niu, C., Xu, K.: Learning part generation and assembly for structure-aware shape synthesis. In: Proceedings of the AAAI conference on artificial intelligence. vol. 34, pp. 11362–11369 (2020)

2020
[10]

ACM Transactions on Graphics (TOG) 36(4), 1–14 (2017)

Li, J., Xu, K., Chaudhuri, S., Yumer, E., Zhang, H., Guibas, L.: Grass: Genera- tive recursive autoencoders for shape structures. ACM Transactions on Graphics (TOG) 36(4), 1–14 (2017)

2017
[11]

In: Proceedings of the IEEE/CVF conference on computer vision and pattern recog- nition

Luo, S., Hu, W.: Diffusion probabilistic models for 3d point cloud generation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recog- nition. pp. 2837–2845 (2021)

2021
[12]

Scientific Data9(1), 397 (2022)

Lyu, X., Cheng, L., Zhang, S.: The reta benchmark for retinal vascular tree anal- ysis. Scientific Data9(1), 397 (2022)

2022
[13]

Mo, K., Guerrero, P., Yi, L., Su, H., Wonka, P., Mitra, N.J., Guibas, L.J.: Struc- turenet: hierarchical graph networks for 3d shape generation38(6) (2019)

2019
[14]

In: Thirty-Fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2021)

Paetzold, J.C., McGinnis, J., Shit, S., Ezhov, I., Büschl, P., Prabhakar, C., Sekuboyina, A., Todorov, M., Kaissis, G., Ertürk, A., et al.: Whole brain ves- sel graphs: A dataset and benchmark for graph learning and neuroscience. In: Thirty-Fifth Conference on Neural Informati...

2021
[16]

In: Proceedings of the IEEE/CVF International Conference on Computer Vision

Schor, N., Katzir, O., Zhang, H., Cohen-Or, D.: Componet: Learning to generate the unseen by part synthesis and composition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 8759–8768 (2019)

2019
[17]

In: proceedings of Medical Image Computing and Computer Assisted Intervention – MICCAI 2024

Sinha, A., Hamarneh, G.: TrIND: Representing Anatomical Trees by Denoising Diffusion of Implicit Neural Fields . In: proceedings of Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. vol. LNCS 15012. Springer Nature Switzerland (October 2024)

2024
[18]

Socher, R., Huval, B., Bath, B., Manning, C.D., Ng, A.: Convolutional-recursive deeplearningfor3dobjectclassification.Advancesinneuralinformationprocessing systems 25 (2012)

2012
[19]

In: Proceedings of the 28th international conference on machine learning (ICML-11)

Socher, R., Lin, C.C., Manning, C., Ng, A.Y.: Parsing natural scenes and natural language with recursive neural networks. In: Proceedings of the 28th international conference on machine learning (ICML-11). pp. 129–136 (2011)

2011
[20]

Advances in Neural Infor- mation Processing Systems35, 10021–10039 (2022)

Vahdat, A., Williams, F., Gojcic, Z., Litany, O., Fidler, S., Kreis, K., et al.: Lion: Latent point diffusion models for 3d shape generation. Advances in Neural Infor- mation Processing Systems35, 10021–10039 (2022)

2022
[21]

Meta-Radiology2(4), 100102 (2024)

Wang, Z., et al.: Cardiovascular medical image and analysis based on 3d vision: A comprehensive survey. Meta-Radiology2(4), 100102 (2024)

2024
[22]

Meta-Radiology p

Wang, Z., Yi, R., Wen, X., Zhu, C., Xu, K.: Cardiovascular medical image and analysis based on 3d vision: A comprehensive survey. Meta-Radiology p. 100102 (2024)

2024
[23]

arXiv preprint arXiv:1804.04381 (2018)

Wolterink, J.M., Leiner, T., Isgum, I.: Blood vessel geometry synthesis using gen- erative adversarial networks. arXiv preprint arXiv:1804.04381 (2018)

2018 arXiv
[24]

Computational Visual Media9(3), 407–442 (2023)

Xu, Q.C., Mu, T.J., Yang, Y.L.: A survey of deep learning-based 3d shape gener- ation. Computational Visual Media9(3), 407–442 (2023)

2023
[25]

In: Proceedings of the IEEE/CVF international conference on computer vision

Yang, G., Huang, X., Hao, Z., Liu, M.Y., Belongie, S., Hariharan, B.: Pointflow: 3d point cloud generation with continuous normalizing flows. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 4541–4550 (2019) Hierarchical Part-based Generative Mo...

2019
[26]

ArXiv pp

Yang, K., Musio, F., Ma, Y., Juchler, N., Paetzold, J.C., Al-Maskari, R., Höher, L., Li, H.B., Hamamci, I.E., Sekuboyina, A., et al.: Benchmarking the cow with the topcow challenge: Topology-aware anatomical segmentation of the circle of willis for cta and mra. ArXiv pp. arXiv...

2024
[27]

IEEE transactions on medical imaging42(11), 3155–3166 (2023)

Yao, L., Shi, F., Wang, S., Zhang, X., Xue, Z., Cao, X., Zhan, Y., Chen, L., Chen, Y., Song, B., et al.: Tag-net: topology-aware graph network for centerline-based vessel labeling. IEEE transactions on medical imaging42(11), 3155–3166 (2023)

2023
[28]

Computerized Medical Imaging and Graphics 109, 102287 (2023)

Zeng, A., Wu, C., Lin, G., Xie, W., Hong, J., Huang, M., Zhuang, J., Bi, S., Pan, D., Ullah, N., Khan, K.N., Wang, T., Shi, Y., Li, X., Xu, X.: Imagecas: A large- scale dataset and benchmark for coronary artery segmentation based on computed tomography angiography images. Comp...

2023
[29]

In: 34th British Machine Vision Conference 2023, BMVC 2023, Aberdeen, UK, November 20-24, 2023

Zhang, G., Dong, C., Li, Y.: Topology-preserving hard pixel mining for tubular structure segmentation. In: 34th British Machine Vision Conference 2023, BMVC 2023, Aberdeen, UK, November 20-24, 2023. BMVA (2023)

2023
[31]

IEEE Transactions on Neural Net- works and Learning Systems35(4), 5461–5472 (2022)

Zhao, J., Chen, X., Xiong, Z., Zha, Z.J., Wu, F.: Graph representation learning for large-scale neuronal morphological analysis. IEEE Transactions on Neural Net- works and Learning Systems35(4), 5461–5472 (2022)

2022

Pith tools

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

[1] [15]

In: Interna- tional Conference on Medical Image Computing and Computer-Assisted Interven- tion

Prabhakar, C., Shit, S., Musio, F., Yang, K., Amiranashvili, T., Paetzold, J.C., Li, H.B., Menze, B.: 3d vessel graph generation using denoising diffusion. In: Interna- tional Conference on Medical Image Computing and Computer-Assisted Interven- tion. pp. 3–13. Springer (2024)

work page 2024

[2] [30]

In: International Symposium on Bioinformatics Research and Applications

Zhang, G., Li, Y.: A geometric algorithm for blood vessel reconstruction from skeletal representation. In: International Symposium on Bioinformatics Research and Applications. pp. 114–126. Springer (2024)

work page 2024

[3] [1]

In: Proceedings of the 35th International Conference on Machine Learning (ICML)

Achlioptas, P., Diamanti, O., Mitliagkas, I., Guibas, L.J.: Learning representations and generative models for 3d point clouds. In: Proceedings of the 35th International Conference on Machine Learning (ICML). pp. 40–49. PMLR (2018)

work page 2018

[4] [2]

arXiv preprint arXiv:1910.14388 (2019)

Belli,D.,Kipf,T.:Image-conditionedgraphgenerationforroadnetworkextraction. arXiv preprint arXiv:1910.14388 (2019)

arXiv 2019

[5] [3]

arXiv preprint arXiv:1512.03012 (2015)

Chang, A.X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., et al.: Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012 (2015)

arXiv 2015

[6] [4]

In: Computer graphics forum

Chaudhuri, S., Ritchie, D., Wu, J., Xu, K., Zhang, H.: Learning generative models of 3d structures. In: Computer graphics forum. vol. 39, pp. 643–666. Wiley Online Library (2020)

work page 2020

[7] [5]

Dong, C., Xu, S., Dai, D., Zhang, Y., Zhang, C., Li, Z.: A novel multi-attention, multi-scale3ddeepnetworkforcoronaryarterysegmentation.MedicalImageAnal- ysis 85, 102745 (2023)

work page 2023

[8] [6]

In: International Con- ference on Medical Image Computing and Computer-Assisted Intervention

Feldman, P., Fainstein, M., Siless, V., Delrieux, C., Iarussi, E.: Vesselvae: Recur- sive variational autoencoders for 3d blood vessel synthesis. In: International Con- ference on Medical Image Computing and Computer-Assisted Intervention. pp. 67–76. Springer (2023)

work page 2023