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REVIEW 3 major objections 5 minor 28 references

GLCP: Global-to-Local Connectivity Preservation for Tubular Structure Segmentation

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

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Pith's one-line read This paper claims that structural fragmentation in vessel segmentation is best reduced by making the model predict its own break-prone spots—redundant endpoints in its predicted skeleton—and then refine the mask with those discontinuity…

desk verdict A solid within-subfield method paper that deserves review, with one self-supervised label-source gap that should be probed before publication. read the letter →

arxiv 2507.21328 v1 pith:JH6F6CYX submitted 2025-07-28 cs.CV

Feixiang Zhou , Zhuangzhi Gao , He Zhao , Jianyang Xie , Yanda Meng , Yitian Zhao , Gregory Y.H. Lip , Yalin Zheng This is my paper · ORCID
classification cs.CV
keywords TubularstructuresegmentationVascularConnectivitypreservationDiscontinuitymapSkeletonpredictionDual-attentionrefinementnnU-NetBettierror
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 structural fragmentation in tubular structure segmentation—the broken, gap-riddled vessel masks that plague vascular image analysis—is best fixed by teaching the model to predict where breaks occur, not just by imposing global topology losses. It proposes GLCP, which adds three parallel heads to a standard segmentation network: one for the global segmentation, one for a skeleton map, and one for a local discontinuity map whose training targets are mined from the model's own predicted skeleton endpoints. A self-supervised consistency loss aligns the skeleton derived from the segmentation output with the skeleton head's prediction, and a dual-attention refinement module fuses the skeleton and discontinuity maps into the final mask. On retinal (STARE), coronary CTA (CCA), and Circle of Willis (TopCoW) datasets, the method reports higher Dice and clDice and lower Betti errors than several state-of-the-art topology losses, on both 2D and 3D data.

What carries the argument

The central mechanism is an endpoint-guided discontinuity mining strategy that converts skeletons into training targets for a dedicated head: endpoints are extracted from predicted and ground-truth skeletons via a fixed convolution kernel, distances between them are computed, endpoints with distance greater than the mean plus one standard deviation are selected, DBSCAN merges nearby candidates into clusters, and the chosen points are expanded into local cube windows to form the discontinuity ground truth. The Interactive Multi-head Segmentation (IMS) module—three parallel heads on a shared backbone—produces segmentation, skeleton, and discontinuity maps, with a gradient-truncated symmetric KL-divergence consistency loss between the segmentation-derived skeleton and the skeleton head's output. The Dual-Attention-based Refinement (DAR) module then uses sigmoid-activated skeleton and discontinuity maps as attention weights added to the segmentation feature to produce the final refined map.

What would settle it

Train GLCP with the discontinuity masks replaced by labels derived only from ground-truth skeletons (or with the dynamic threshold shifted to mean, mean plus 0.5 standard deviations, and mean plus two standard deviations); if the reported Betti-error gains persist across these variations, the self-referential pseudo-labeling is robust, and if they collapse, the improvement is an artifact of the self-generated targets.

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Extended reading notes

Core claim

The central claim is that local discontinuity awareness, rather than additional global topological constraints, is the missing ingredient for connectivity in tubular structures. To make this concrete, the paper introduces a discontinuity prediction head whose training targets are mined from the network's own current predictions: skeleton endpoints of the predicted mask that lie far from the ground-truth skeleton (more than mean plus one standard deviation of all such distances) are labeled as discontinuities, clustered by DBSCAN, and expanded into small cube regions that the head must learn to flag. A parallel skeleton head learns the global vessel tree, and a symmetric KL-divergence loss with gradient truncation forces the skeleton derived from the segmentation output and the skeleton head's output to agree. The refined segmentation then linearly combines the segmentation with skeleton and discontinuity attention maps. The paper reports that this combination outperforms existing loss-based and multi-task baselines across all datasets, with the discontinuity task contributing the largest share of the topology improvement in ablations.

Load-bearing premise

The auxiliary discontinuity labels are produced from the model's own predicted skeletons via a dynamic endpoint-distance threshold, assuming those self-generated targets reliably mark real fragmentation and that training on them improves generalization instead of reinforcing the model's current errors.

Editorial extensions

If this is right

  • On STARE, CCA, and TopCoW (binary and multi-class), adding GLCP to nnU-Net V2 and SwinUNETR improves Dice and clDice over the respective baselines and over clDice, cbDice, and skeleton-recall loss additions.
  • Betti errors (sum of $\beta_0$ and $\beta_1$) drop on every tested dataset, indicating fewer disconnected fragments and fewer spurious holes in the predicted vessel topology.
  • Ablation shows the discontinuity prediction task is the dominant contributor to the topology improvement, with the skeleton task, consistency loss, and DAR refinement each adding smaller gains.
  • The method transfers to a transformer-based backbone (SwinUNETR), so the benefit is not specific to the convolutional nnU-Net architecture.

Reading between the lines

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

  • Because the discontinuity labels are generated from the model's own predicted skeleton endpoints, the framework implicitly assumes the model's current errors are visible in its skeleton; an untested alternative would replace these self-derived labels with oracle labels from the ground-truth skeleton to see whether the reported gains shrink.
  • The predicted discontinuity map at inference could be repurposed as an uncertainty signal for downstream review, flagging exactly where the predicted vessel tree is fragile.
  • The dynamic threshold (mean plus one standard deviation) is a single design choice; a sensitivity analysis across thresholds (e.g., mean, mean plus 0.5 standard deviations, mean plus two standard deviations) would show whether the method's performance depends on the exact statistics or is robust to them.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper proposes GLCP, a multi-head framework for tubular structure segmentation. It augments a shared nnUNet or SwinUNETR backbone with a skeleton head and a discontinuity head, plus a lightweight refinement module DAR. The discontinuity head is trained from masks constructed online by detecting endpoints in the current predicted skeleton and thresholding their distance to ground-truth skeleton endpoints (Eqs. 1-2), then expanding selected points into cube windows. A KL consistency loss aligns the skeleton head with skeletons derived from the main segmentation output, and DAR uses skeleton and discontinuity probability maps as attention to refine the prediction. Experiments on STARE, CCA, and TopCoW compare Dice, clDice, Betti error, and HD against several loss functions and architectures, with ablations in Table 3. The authors claim superior accuracy and connectivity across 2D and 3D tubular segmentation tasks.

Significance. If the claimed improvements hold, the framework is a practical contribution: it integrates into nnUNet by adding only four lightweight convolution layers, is evaluated on 2D and 3D datasets including a multi-class setting, and consistently lowers Betti errors in the reported comparisons. The authors also test transfer to SwinUNETR, which supports generalizability. The idea of explicitly localizing discontinuities through endpoint mining is well motivated and clearly presented. However, because the auxiliary discontinuity labels are generated from the model's own output during training, the reported gains are not yet isolated from self-training effects or extra head capacity, and the small test sets without error bars or significance tests limit the strength of the central claim.

major comments (3)
  1. [Section 2.1, Eqs. (1)-(2); Section 3.3, Table 3] The ground-truth discontinuity mask is generated from the current predicted segmentation: predicted skeleton endpoints are compared to ground-truth skeleton endpoints, and endpoints with distance greater than mean plus standard deviation are selected. The figure caption states that SE and EDM are used only for training, but this does not address the more important concern that the pseudo-labels are a function of the model's own output at every iteration. The paper does not ablate the label-generation strategy (e.g., labels from a frozen pretrained network, labels derived from GT alone, or random labels with matched density), nor does it report how the selected endpoint set evolves as training proceeds. Consequently, the Betti error reductions in Table 3 (e.g., STARE from 4.60 to 3.00, TopCoW-multi from 0.57 to 0.39) are confounded with extra head capacity and implicit regularization, so the attribution of the improvement to the global-to-local connectivity mechanism itself is not yet established.
  2. [Section 3.3, Tables 1-2] The test sets are small (STARE has 10 test images, TopCoW has 18, CCA has 4) and the paper reports no standard deviations, multiple seeds, or significance tests. Several reported differences are small in absolute terms, such as STARE Dice 83.67 versus 83.39 and clDice 87.44 versus 87.11, and TopCoW-binary clDice 95.58 versus 95.57. With single runs, these differences could easily be within run-to-run variation, especially for Betti errors computed on only four CCA images. The authors should provide at least multiple-seed means with standard deviations or statistical significance tests, or temper the 'superior' and 'outperforms' wording accordingly.
  3. [Section 3.2, Eq. (2)] Several free parameters are fixed without sensitivity analysis: the weight factors alpha and beta are both set to 0.5, the cube window size Ri is set to one-eighth of the patch size, and the dynamic threshold in Eq. (2) is fixed as mean plus standard deviation, with DBSCAN parameters unspecified. Since the threshold and window size directly determine the auxiliary discontinuity supervision, the reported improvements may rely on a favorable choice of these parameters. A sensitivity study over reasonable ranges, at least for the threshold formula and the loss weights, would demonstrate robustness and make the method easier to adopt on new datasets.
minor comments (5)
  1. [Eq. (3)] In the definition of the cube window, the third coordinate condition is written as '|y - pz_i|' but should presumably be '|z - pz_i|'; please correct this typo.
  2. [Eq. (4) and Eq. (5)] The symbol '⊗' is not defined; specify whether it denotes element-wise multiplication or some other tensor operation, since it is used in both the consistency loss and the refinement formulation.
  3. [Tables 1-2 and text] The dataset name is spelled 'TopCoW' in the abstract and most of the text but 'ToPCoW' in Tables 1 and 2; please unify the spelling.
  4. [Section 3.3] The text says Table 1 compares five other state-of-the-art loss functions, but the table also includes a multi-decoder framework and a transformer-based architecture; please adjust the wording to match the table content.
  5. [Section 3.1] For the 3D datasets, please state whether Betti errors and HD are computed per-case and then averaged or computed on aggregated volumes, so that the metrics are reproducible.

Circularity Check

0 steps flagged · score 0.0 of 10

No construction-level circularity: the auxiliary discontinuity labels are generated from the model's own predictions, but all reported metrics are evaluated against real ground truth, and no load-bearing self-citation chain exists.

full rationale

The paper's central claim is an empirical segmentation result, and every headline metric (Dice, clDice, Betti error, HD) is computed against external ground-truth annotations on STARE, TopCoW, and CCA. The only self-referential mechanism is the endpoint-guided discontinuity mining in Sec. 2.1: Eqs. (1)-(2) construct the auxiliary discontinuity targets Fd from skeleton endpoints of the current predicted mask F̂g and the GT skeleton, and this target is then used to train the discontinuity head and to form the DAR attention in Eq. (5). This is a disclosed self-training/bootstrap signal, not a fitted parameter renamed as the reported prediction: the paper does not claim the discontinuity map itself as an evaluated scientific output, and the final segmentation remains trained and assessed against GT. No uniqueness theorem, load-bearing self-citation, or ansatz smuggled in via citation was found; references to the authors' prior work appear only as related work or baselines and do not carry the argument. The online pseudo-label strategy and small test sets are limitations for generalization and statistical robustness, but they do not make the central derivation circular by construction.

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

The central claim depends on several human-chosen hyperparameters and domain assumptions about skeleton extraction and endpoint detection. The pseudo-label generation from model predictions introduces a self-referential process that is not independently validated.

free parameters (5)
  • alpha (weight for consistency loss) = 0.5
    Set by hand in Section 3.2; no sensitivity analysis.
  • beta (weight for DAR loss) = 0.5
    Set by hand in Section 3.2; no sensitivity analysis.
  • Ri (cube window size for discontinuity masks) = one-eighth of input patch size
    Chosen in Section 3.2 without ablation.
  • Dynamic threshold tau = mean(D_g) + std(D_g)
    Hand-crafted statistic in Eq. 2; no justification or sensitivity analysis.
  • DBSCAN distance parameter = not specified
    Used to cluster discontinuity points in Section 2.1 but no value is given.
assumptions (4)
  • domain assumption Skeleton extraction (cit. [20,22]) correctly captures the topology of tubular structures.
    Used to generate skeleton ground truths and predicted skeletons in Section 2.1.
  • domain assumption Endpoint detection via a fixed convolution kernel reliably identifies all skeleton endpoints.
    Used to build sets P_g and Phat_g in Section 2.1.
  • domain assumption nnUNet's default training settings are suitable for the auxiliary tasks in all datasets.
    The paper follows nnUNet defaults for optimization (Section 3.2) without adapting them for multi-head training.
  • ad hoc to paper KL divergence between softmax of segmentation-skeleton and skeleton prediction is a valid consistency measure.
    Introduced in Eq. 4 as a self-supervised consistency loss.

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Cite this review

Pith. "Pith review of GLCP: Global-to-Local Connectivity Preservation for Tubular Structure Segmentation." pith.science (2026). https://pith.science/paper/JH6F6CYX

@misc{pith2026250721328,
  author       = {Pith},
  title        = {Pith review of: GLCP: Global-to-Local Connectivity Preservation for Tubular Structure Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JH6F6CYX}},
  note         = {Machine review of arXiv:2507.21328}
}
read the original abstract

Accurate segmentation of tubular structures, such as vascular networks, plays a critical role in various medical domains. A remaining significant challenge in this task is structural fragmentation, which can adversely impact downstream applications. Existing methods primarily focus on designing various loss functions to constrain global topological structures. However, they often overlook local discontinuity regions, leading to suboptimal segmentation results. To overcome this limitation, we propose a novel Global-to-Local Connectivity Preservation (GLCP) framework that can simultaneously perceive global and local structural characteristics of tubular networks. Specifically, we propose an Interactive Multi-head Segmentation (IMS) module to jointly learn global segmentation, skeleton maps, and local discontinuity maps, respectively. This enables our model to explicitly target local discontinuity regions while maintaining global topological integrity. In addition, we design a lightweight Dual-Attention-based Refinement (DAR) module to further improve segmentation quality by refining the resulting segmentation maps. Extensive experiments on both 2D and 3D datasets demonstrate that our GLCP achieves superior accuracy and continuity in tubular structure segmentation compared to several state-of-the-art approaches. The source codes will be available at https://github.com/FeixiangZhou/GLCP.

Figures

Figures reproduced from arXiv: 2507.21328 by the authors.

Figure 1
Figure 1. Illustration of motivation. In most existing methods, structural fragmentation (red boxes) commonly occurs in predicted masks, manifesting as redundant endpoints (indicated by blue arrows). We propose to directly focus on these regions during training by adaptively learning discontinuity maps, allowing the model to better capture local structural characteristics for connectivity enhancement. For clear visualization,… view at source ↗
Figure 2
Figure 2. An overview of the proposed GLCP. Left) The input patch is first fed into an encoder-decoder backbone to extract image representations. Next, an IMS module uses the encoded representations as input to produce segmentation, discontinuity and skeleton maps, based on which we design a DAR module to integrate global skeleton attention with local discontinuity attention for refining segmentation results. Right) In EDM, e… view at source ↗
Figure 3
Figure 3. Qualitative results on the STARE, ToPCoW-binary, CCA and ToPCoW-multi datasets (listed from top to bottom). Blue, green, and red arrows indicate regions of false negatives, false positives, and misclassifications, respectively, highlighting the challenges in segmentation and the improvements achieved by our method. multi-class segmentation tasks, as shown in [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

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Works this paper leans on

28 extracted references · 23 canonical work pages

  1. [1]

    International Journal of Integrated Engineering 13(1), 188–200 (2021)

    Alirr, O.I., Abd Rahni, A.A.: An automated liver vasculature segmentation from ct scans for hepatic surgical planning. International Journal of Integrated Engineering 13(1), 188–200 (2021)

    work page 2021

  2. [2]

    IEEE transactions on pattern analysis and machine intelligence 44(12), 8766–8778 (2020)

    Clough, J.R., Byrne, N., Oksuz, I., Zimmer, V.A., Schnabel, J.A., King, A.P.: A topological loss function for deep-learning based image segmentation using per- sistent homology. IEEE transactions on pattern analysis and machine intelligence 44(12), 8766–8778 (2020)

    work page 2020

  3. [3]

    Medical Image Analysis 78, 102389 (2022)

    Dong, S., Pan, Z., Fu, Y., Yang, Q., Gao, Y., Yu, T., Shi, Y., Zhuo, C.: Deu-net 2.0: Enhanced deformable u-net for 3d cardiac cine mri segmentation. Medical Image Analysis 78, 102389 (2022)

    work page 2022

  4. [4]

    In: International MICCAI brainlesion workshop

    Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI brainlesion workshop. pp. 272–284. Springer (2021)

    2021

  5. [5]

    IEEE transactions on medical imaging 22(8), 951–958 (2003)

    Hoover, A., Goldbaum, M.: Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE transactions on medical imaging 22(8), 951–958 (2003)

    2003

  6. [6]

    Advances in neural information processing systems32 (2019)

    Hu, X., Li, F., Samaras, D., Chen, C.: Topology-preserving deep image segmenta- tion. Advances in neural information processing systems32 (2019)

    work page 2019

  7. [7]

    In: European Conference on Computer Vision

    Huang, J., Zhou, Y., Luo, Y., Liu, G., Guo, H., Yang, G.: Representing topologi- cal self-similarity using fractal feature maps for accurate segmentation of tubular structures. In: European Conference on Computer Vision. pp. 143–160. Springer (2025)

    work page 2025

  8. [8]

    PLoS computational biology16(6), e1007943 (2020) 10 Zhou et al

    Ii, S., Kitade, H., Ishida, S., Imai, Y., Watanabe, Y., Wada, S.: Multiscale modeling of human cerebrovasculature: A hybrid approach using image-based geometry and a mathematical algorithm. PLoS computational biology16(6), e1007943 (2020) 10 Zhou et al

    work page 2020

Show all 28 references
  1. [9]

    Nature methods 18(2), 203–211 (2021)

    Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods 18(2), 203–211 (2021)

    2021

  2. [10]

    In: The fifth international conference on the applications of digital information and web technologies (ICADIWT 2014)

    Khan, K., Rehman, S.U., Aziz, K., Fong, S., Sarasvady, S.: Dbscan: Past, present and future. In: The fifth international conference on the applications of digital information and web technologies (ICADIWT 2014). pp. 232–238. IEEE (2014)

    2014

  3. [11]

    In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G

    Kirchhoff, Y., Rokuss, M.R., Roy, S., Kovacs, B., Ulrich, C., Wald, T., Zenk, M., Vollmuth, P., Kleesiek, J., Isensee, F., Maier-Hein, K.: Skeleton recall loss for con- nectivity conserving and resource efficient segmentation of thin tubular structures. In: Leonardis, A., Ricc...

    2024

  4. [12]

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

    Li, L., Ma, Q., Ouyang, C., Li, Z., Meng, Q., Zhang, W., Qiao, M., Kyriakopoulou, V., Hajnal, J.V., Rueckert, D., et al.: Robust segmentation via topology violation detection and feature synthesis. In: International Conference on Medical Image Computing and Computer-Assisted I...

    2023

  5. [13]

    IEEE Transactions on Cyber- netics 53(9), 5826–5839 (2022)

    Li, Y., Zhang, Y., Liu, J.Y., Wang, K., Zhang, K., Zhang, G.S., Liao, X.F., Yang, G.: Global transformer and dual local attention network via deep-shallow hierar- chical feature fusion for retinal vessel segmentation. IEEE Transactions on Cyber- netics 53(9), 5826–5839 (2022)

    2022

  6. [14]

    arXiv preprint arXiv:2404.18539 (2024)

    Liu, C., Ma, B., Ban, X., Xie, Y., Wang, H., Xue, W., Ma, J., Xu, K.: Enhanc- ing boundary segmentation for topological accuracy with skeleton-based methods. arXiv preprint arXiv:2404.18539 (2024)

    2024 arXiv

  7. [15]

    IEEE transactions on medical imaging40(3), 928–939 (2020)

    Ma, Y., Hao, H., Xie, J., Fu, H., Zhang, J., Yang, J., Wang, Z., Liu, J., Zheng, Y., Zhao, Y.: Rose: a retinal oct-angiography vessel segmentation dataset and new model. IEEE transactions on medical imaging40(3), 928–939 (2020)

    2020

  8. [16]

    In: Medical Image Computing and Computer Assisted Intervention– MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part I 22

    Mou, L., Zhao, Y., Chen, L., Cheng, J., Gu, Z., Hao, H., Qi, H., Zheng, Y., Frangi, A., Liu, J.: Cs-net: Channel and spatial attention network for curvilinear structure segmentation. In: Medical Image Computing and Computer Assisted Intervention– MICCAI 2019: 22nd Internationa...

    2019

  9. [17]

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

    Qi, X., Yang, G., He, Y., Liu, W., Islam, A., Li, S.: Contrastive re-localization and history distillation in federated cmr segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 256–265. Springer (2022)

    2022

  10. [18]

    In: Proceed- ings of the IEEE/CVF International Conference on Computer Vision

    Qi, Y., He, Y., Qi, X., Zhang, Y., Yang, G.: Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation. In: Proceed- ings of the IEEE/CVF International Conference on Computer Vision. pp. 6070– 6079 (2023)

    2023

  11. [19]

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

    Shi,P., Hu,J., Yang,Y., Gao,Z.,Liu, W.,Ma, T.:Centerlineboundarydiceloss for vascular segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 46–56. Springer (2024)

    2024

  12. [20]

    In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Shit, S., Paetzold, J.C., Sekuboyina, A., Ezhov, I., Unger, A., Zhylka, A., Pluim, J.P., Bauer, U., Menze, B.H.: cldice-a novel topology-preserving loss function for tubular structure segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recog...

    2021

  13. [21]

    BMC medical imaging15, 1–28 (2015)

    Taha, A.A., Hanbury, A.: Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool. BMC medical imaging15, 1–28 (2015)

    2015

  14. [22]

    PeerJ 2, e453 (2014) GLCP: Global-to-Local Connectivity Preservation 11

    Van der Walt, S., Schönberger, J.L., Nunez-Iglesias, J., Boulogne, F., Warner, J.D., Yager, N., Gouillart, E., Yu, T.: scikit-image: image processing in python. PeerJ 2, e453 (2014) GLCP: Global-to-Local Connectivity Preservation 11

    2014

  15. [23]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog- nition

    Wang, Y., Wei, X., Liu, F., Chen, J., Zhou, Y., Shen, W., Fishman, E.K., Yuille, A.L.: Deep distance transform for tubular structure segmentation in ct scans. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog- nition. pp. 3833–3842 (2020)

    2020

  16. [24]

    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

  17. [25]

    IEEE Transactions on Medical Imaging (2024)

    Yang, X., Xu, L., Yu, S., Xia, Q., Li, H., Zhang, S.: Segmentation and vascular vec- torization for coronary artery by geometry-based cascaded neural network. IEEE Transactions on Medical Imaging (2024)

    2024

  18. [26]

    Multimedia Tools and Applications 81(11), 15593–15607 (2022)

    Yang, X., Li, Z., Guo, Y., Zhou, D.: Dcu-net: A deformable convolutional neural network based on cascade u-net for retinal vessel segmentation. Multimedia Tools and Applications 81(11), 15593–15607 (2022)

    2022

  19. [27]

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

    Zhang, X., Zhang, J., Ma, L., Xue, P., Hu, Y., Wu, D., Zhan, Y., Feng, J., Shen, D.: Progressive deep segmentation of coronary artery via hierarchical topology learn- ing. In: International Conference on Medical Image Computing and Computer- Assisted Intervention. pp. 391–400....

    2022

  20. [28]

    Pattern Recognition98, 107068 (2020)

    Zhao, H., Li, H., Cheng, L.: Improving retinal vessel segmentation with joint local loss by matting. Pattern Recognition98, 107068 (2020)

    2020

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