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REVIEW 3 major objections 5 minor 2 cited by

vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A single model trained on three heterogeneous data sources can segment 3D blood vessels in unseen imaging modalities, outperforming specialist and general-purpose baselines without fine-tuning.

desk verdict Solid empirical contribution with a genuinely useful open model; the headline zero-shot margins on two datasets rest on very few test volumes and need a TriSAM comparison. read the letter →

arxiv 2411.17386 v2 pith:ZEVLWRTM submitted 2024-11-26 eess.IV cs.CV

Bastian Wittmann , Yannick Wattenberg , Tamaz Amiranashvili , Suprosanna Shit , Bjoern Menze This is my paper · ORCID
classification eess.IVcs.CV
keywords 3Dbloodvesselsegmentationfoundationmodelzero-shotgeneralizationdomainrandomizationflowmatchingsyntheticmedicalimagesimagetubularstructure
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

vesselFM is a foundation model built specifically for 3D blood vessel segmentation. The paper's central claim is that training a single segmentation network on three deliberately heterogeneous data sources — a large curated collection of real vascular images, a domain-randomized synthetic corpus, and images sampled from a flow-matching generative model — produces a model that segments vessels in imaging domains it has never seen, without any fine-tuning. The authors report that this model outperforms four state-of-the-art segmentation foundation models across four (pre-)clinically relevant modalities in zero-, one-, and few-shot settings, with zero-shot Dice of 74.66 on ultra-high-field MRA and 67.49 on volume electron microscopy versus next-best scores of 48.32 and 10.92. If true, the implication is that a single open-sourced model could replace dataset-specific annotation and training pipelines for vascular imaging, including for new modalities where annotated data does not yet exist. The paper's method rests on the assumption that corrosion-cast vascular geometry is a bias-free prior for vascular morphology across modalities.

What carries the argument

The load-bearing machinery is the three-source training distribution, not a new network design. The segmentation model is a standard 3D U-Net (DynUNet-style, 31.4M parameters). What carries the argument is (1) Dreal, more than 115,000 curated $128^{3}$ real image-mask patches spanning 23 datasets and 17 sources; (2) Ddrand, a domain-randomization pipeline in which foreground masks are generated by applying random spatial and artifact transformations to 1,137 corrosion-cast vascular patches and merged into Perlin-noise, Voronoi, or plain backgrounds with a wide range of intensity transformations; and (3) Dflow, synthetic images drawn from a mask- and class-conditioned flow-matching generative model, which learns a velocity field mapping Gaussian noise to data through an ordinary differential equation and is conditioned on the same synthetic masks from Msyn. Together these sources are meant to cover the full domain of 3D vascular images and to broaden Dreal's distribution in a data-driven way.

What would settle it

Apply vesselFM zero-shot to a 3D vascular dataset whose contrast mechanism is unlike anything in Dreal, Ddrand, or Dflow — for example, human retinal OCTA or coronary X-ray angiography — and compare its Dice against the four baselines from Table 2; the paper's universality claim predicts vesselFM should remain well above the next-best baseline without fine-tuning, while a collapse to near-baseline performance would falsify the claim that the synthetic vascular prior covers genuinely new domains.

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Extended reading notes

Core claim

On its own terms, the paper's discovery is that the failure of existing foundation models to segment blood vessels is a data-distribution problem, not a network-capacity problem. By replacing a single homogeneous training set with three heterogeneous sources — real annotated volumes, domain-randomized synthetic volumes, and volumes sampled from a mask- and class-conditioned flow-matching generator — the same 3D U-Net becomes a universal vessel segmenter. The paper reports zero-shot Dice of 46.94 on OCTA, 67.49 on BvEM, 74.66 on SMILE-UHURA, and 29.69 on MSD8, each above every baseline; on BvEM the next-best baseline scores 10.92. One- and few-shot fine-tuning with one or three $128^{3}$ patches improves these scores further, and ablations show each data source contributes: adding Ddrand and Dflow to Dreal raises SMILE-UHURA zero-shot Dice from 65.45 to 74.66. The authors conclude that vesselFM provides an out-of-the-box solution for 3D blood vessel segmentation across (pre-)clinically relevant modalities.

Load-bearing premise

The load-bearing premise is that the 1,137 corrosion-cast vascular patches used to generate synthetic masks capture the general shape, scale, and connectivity properties of blood vessels well enough that a model trained on those masks transfers to modalities with entirely different contrast mechanisms, such as OCTA flow signal and electron-microscopy ultrastructure.

Editorial extensions

If this is right

  • One open-sourced model can replace dataset-specific training for 3D vessel segmentation in unseen modalities, including modalities whose contrast mechanism (OCTA, vEM) is absent from training.
  • Bootstrapping annotations becomes practical: fine-tuning with a single 128^3 patch improves vesselFM's Dice on BvEM from 67.49 to 78.27 and on OCTA from 46.94 to 72.10, so clinicians can adapt the model to a new protocol with minimal labeling.
  • The two synthetic sources are not interchangeable with real data: ablations show Dreal alone reaches 65.45 Dice on SMILE-UHURA, and adding Ddrand and Dflow adds 9.21 Dice, implying that synthetic data generation is a necessary part of the recipe, not a convenience.
  • General-purpose medical segmentation foundation models (SAM-Med3D, MedSAM-2, VISTA3D) are not reliable zero-shot vessel segmenters; a task-specific foundation model with a tubular inductive bias is needed.
  • Because the generative model outperforms a diffusion baseline (Med-DDPM) by 4.32 Dice when used as a data source, flow matching is claimed as the better synthetic-image engine for this task.

Reading between the lines

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

  • The same three-source recipe — real curated data, a geometric prior from corrosion casts, and a generative model — may transfer to other tubular structures such as axons, bile ducts, or lymphatics, since the paper observes the model already segments axons and colon; this is an extension, not a paper claim.
  • The reported universality is bounded by the evaluation domains; a stronger test would be to apply vesselFM zero-shot to modalities with very different contrast, such as human retinal OCTA or X-ray angiography, which the paper does not report.
  • The synthetic-mask prior could be validated more directly by comparing vessel-radius and tortuosity statistics of Msyn against ground truth in the four evaluation datasets; if the statistics mismatch, the zero-shot margin would be expected to shrink.
  • The 70/20/10 sampling weights and 128^3 patch size are engineering choices whose sensitivity is untested; an independent replication varying these would reveal how robust the zero-shot margin is.
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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 vesselFM, a 3D blood vessel segmentation foundation model trained on three heterogeneous data sources: a curated real dataset Dreal (23 datasets, >115k patches), a domain-randomized synthetic dataset Ddrand (500k pairs), and a flow-matching-generated dataset Dflow (10k pairs). The authors claim zero-shot, one-shot, and few-shot generalization to four unseen evaluation datasets (SMILE-UHURA, BvEM, OCTA, MSD8), outperforming strong baselines (tUbeNet, VISTA3D, SAM-Med3D, MedSAM-2) by large margins, and they provide extensive ablations of data sources, the generative model, and the segmentation architecture.

Significance. The paper makes a potentially valuable empirical contribution: it assembles the largest curated real dataset for 3D vessel segmentation, introduces a domain-randomization pipeline tailored to vascular geometry, extends flow matching to mask- and class-conditioned medical image generation, and shows that the combination improves zero-shot transfer. The open-source code and checkpoints, the systematic ablations, the inclusion of clDice as a topology-aware metric, and the comparison to specialist models are genuine strengths. If the reported generalization holds under more rigorous evaluation, the work would be a useful resource for the community. The main uncertainty is statistical: the most dramatic margins (BvEM zero-shot 67.49 vs. next-best 10.92, OCTA zero-shot 46.94 vs. next-best 36.01) rest on very few test volumes from single acquisitions, and Table 2 reports no variance or confidence intervals.

major comments (3)
  1. [Section 4.1, Table 2; Suppl. A; Suppl. K] The headline claim of 'outperforming all baselines by a large margin' is quantified on very small, non-independent test sets. According to Suppl. A, the BvEM test set consists of three 500^3 subvolumes extracted from a single volume, and the OCTA test set consists of two volumes. Table 2 reports only point estimates, with no per-volume results, standard deviations, or confidence intervals. The paired t-tests in Suppl. K with n=2 (OCTA) and n=3 (BvEM) are not reliable for population inference, and the BvEM subvolumes are not independent samples. This is load-bearing because the zero-shot BvEM and OCTA entries drive the 'large margin' claim. Please report per-subject/per-volume scores, provide confidence intervals (e.g., bootstrap over volumes), and either perform a full-volume BvEM evaluation or explicitly qualify the claim as limited to these small test sets.
  2. [Section 4.2, Table 6, Fig. 9] The ablation claim that Dflow is 'consistently beneficial' is not fully supported by the detailed results. Table 6 shows that on OCTA zero-shot, Dreal+Ddrand achieves Dice 47.02 while Dreal+Ddrand+Dflow achieves 46.94, and on MSD8 zero-shot the corresponding scores are 29.84 and 29.69. The text in Section 4.2 and Fig. 9 relies on an average across datasets, but two of four datasets show small degradations. To support the claim, please report per-dataset variability and either temper the wording or apply a statistical test that accounts for dataset-level variation. This matters because the contribution of Dflow is a central methodological component.
  3. [Section 3.2; Section 4.1] The synthetic vascular prior is asserted to 'accurately preserve both general angioarchitectural and morphological properties characteristic of 3D blood vessels' without validation against an independent geometric benchmark. Since BvEM and OCTA are both mouse-brain datasets and the Ddrand masks originate from mouse-brain corrosion casts, the zero-shot transfer to these modalities could be partly attributable to a species/organ match rather than to universal vascular priors. A concrete test would be to evaluate on a non-mouse-brain vEM or OCTA dataset, or to include a quantitative topological/morphological comparison between Msyn and the target-domain annotations. The large margins on SMILE-UHURA and MSD8 partially mitigate this concern, but the most dramatic zero-shot results (BvEM, OCTA) are exactly the ones affected.
minor comments (5)
  1. [Table 1] The table lists BvEM with 21,858 patches, which may be confusing because the text states that the first four datasets are exclusively evaluation datasets and are excluded from Dreal. Please clarify that the patch count for evaluation datasets is informational only and does not indicate inclusion in Dreal.
  2. [Section 4.2, Table 5] The claim that the chosen UNet 'surpasses' transformer-based networks is technically true but the margin over SwinUNETR-V2 (Dice 74.66 vs. 74.54, clDice 75.27 vs. 74.80) is very small. Please report significance or avoid wording that implies a meaningful superiority over this near-tie.
  3. [Suppl. K] The statistical analysis reports only p-values without test statistics, degrees of freedom, or effect sizes. For n=2 or n=3, please provide the actual paired differences and a description of the non-independence of BvEM subvolumes; the current presentation is too terse to be interpretable.
  4. [Suppl. I] The authors acknowledge that vesselFM's tendency to segment unlabeled tubular structures may 'artificially deflate' its reported Dice. This is an honest and important caveat, but it also underscores the need for per-volume results and possibly a secondary evaluation that accounts for over-segmentation (e.g., relaxed ground truth or human review).
  5. [Throughout] There are minor typographical issues, including 'V oronoi' (Section 3.2), 'HR-Kindney' (Suppl. A), and inconsistent use of 'vascular' vs. 'blood vessel' terminology. These do not affect the technical content.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: vesselFM's central claims are supported by held-out test evaluation, not by construction from training data.

full rationale

This is an empirical model paper rather than a derivation chain, and I find no circular step that reduces a claimed prediction to its own inputs. The central claim—that vesselFM outperforms baseline foundation models in zero-, one-, and few-shot 3D blood vessel segmentation—is supported by Dice and clDice scores on four held-out datasets (SMILE-UHURA, BvEM, OCTA, MSD8). The paper explicitly excludes the classes of these evaluation datasets from Dreal and Dflow (Section 4), and Suppl. A describes how test volumes are extracted from portions of the data not used for fine-tuning. The synthetic data sources Ddrand and Dflow are training-set augmentation pipelines: Dflow is generated by a flow-matching model F trained on Dreal and Ddrand and conditioned on masks Msyn, but this is a data-generation loop, not a logical reduction, because the evaluation measures segmentation of real volumes with ground-truth labels that are not used to fit the segmentation model or to define the reported metrics. The author-overlap citations, notably [54] for the corrosion-cast vascular patches V and [17] for OCTA data, are data sources with independent provenance rather than load-bearing theorems; the paper also empirically ablates the contribution of these sources (Tables 3, 4, 6, Fig. 9), so the results do not rest on an unverified self-citation. The statistical fragility of the BvEM and OCTA test sets—three subvolumes from one BvEM volume and two OCTA test volumes, with paired t-tests at n=2–3 in Suppl. K—is a legitimate robustness and evaluation-risk concern, but it is not a form of circularity. No fitted parameter is renamed as a prediction, no uniqueness theorem is imported from the authors' prior work, and no ansatz is smuggled in via citation in a way that makes the reported generalization forced by construction.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical entities, forces, or conserved quantities. It relies on domain assumptions about the transferability of corrosion-cast vasculature geometry and the fidelity of synthetic data sources. The main free parameters are engineering choices in the data generation and training pipelines.

free parameters (3)
  • Data source sampling weights (Ddrand:Dreal:Dflow) = 70:20:10
    Chosen by the authors to roughly match the sizes of the three data sources; not optimized and only indirectly ablated in Table 3 and Fig. 9.
  • Domain randomization transformation parameters = See Fig. 10 (e.g., elastic deformation sigma, zoom ranges, probabilities)
    Hand-tuned parameters in the domain randomization pipeline to balance diversity and realism; stated in Supplementary B.
  • HR-Kidney label improvement thresholds = Intensity Delta = 0.1, Threshold = 0.9, Filter Size = 11
    Algorithm 1 in Supplementary A is an ad hoc post-processing rule applied to convert noisy HR-Kidney labels into training targets.
assumptions (4)
  • domain assumption Corrosion-cast vascular patches V from Wittmann et al. [54] accurately represent the general angioarchitectural and morphological properties of 3D blood vessels across modalities.
    Section 3.2 states that V 'provides the perfect foundation' for generating realistic vascular images; this underpins both Ddrand and the masks used for Dflow.
  • domain assumption Synthetic masks Msyn are free of annotator-induced biases and errors and serve as reliable ground truth for training.
    Section 3.3 claims Msyn 'is devoid of annotator-induced biases and errors'; no independent validation of geometric fidelity is provided.
  • domain assumption The flow matching model F, trained on Dreal and Ddrand, produces image-mask pairs that broaden Dreal without introducing distribution shift artifacts.
    Section 3.3 assumes F's samples are high fidelity; qualitative support is shown in Fig. 5, but no quantitative fidelity metric is reported.
  • standard math Euler integration of the learned velocity field with N=100 steps is a sufficient approximation of the flow matching ODE.
    Eq. (3) discretizes Eq. (1); this is a standard numerical approximation and is not a source of concern.

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Cite this review

Pith. "Pith review of vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation." pith.science (2026). https://pith.science/paper/ZEVLWRTM

@misc{pith2026241117386,
  author       = {Pith},
  title        = {Pith review of: vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZEVLWRTM}},
  note         = {Machine review of arXiv:2411.17386}
}
read the original abstract

Segmenting 3D blood vessels is a critical yet challenging task in medical image analysis. This is due to significant imaging modality-specific variations in artifacts, vascular patterns and scales, signal-to-noise ratios, and background tissues. These variations, along with domain gaps arising from varying imaging protocols, limit the generalization of existing supervised learning-based methods, requiring tedious voxel-level annotations for each dataset separately. While foundation models promise to alleviate this limitation, they typically fail to generalize to the task of blood vessel segmentation, posing a unique, complex problem. In this work, we present vesselFM, a foundation model designed specifically for the broad task of 3D blood vessel segmentation. Unlike previous models, vesselFM can effortlessly generalize to unseen domains. To achieve zero-shot generalization, we train vesselFM on three heterogeneous data sources: a large, curated annotated dataset, data generated by a domain randomization scheme, and data sampled from a flow matching-based generative model. Extensive evaluations show that vesselFM outperforms state-of-the-art medical image segmentation foundation models across four (pre-)clinically relevant imaging modalities in zero-, one-, and few-shot scenarios, therefore providing a universal solution for 3D blood vessel segmentation.

Figures

Figures reproduced from arXiv: 2411.17386 by the authors.

Figure 1
Figure 1. VesselFM is trained in a supervised manner on image [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Schematic distributions of our three data sources [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Slices of images Xreal from Dreal. Dreal contains vascu￾lar images of shape 1283 with matching voxel-level annotations collected from 23 datasets (classes are indicated in red) of diverse imaging modalities, depicting a wide range of anatomical regions. protocols. For ease of reference, each dataset in Dreal is in￾dexed by a unique class c ∈ C = {1, ..., 23} (see [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (12 more)
Figure 4
Figure 4. Figure 4: a) Schematic overview of our domain randomized generative pipeline used to generate [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: a) Sampling of synthetic images Xflow via our mask- and class-conditioned flow matching-based generative model. We explicitly show our sampling scheme, mapping a sample x0 ∼ N (0, I) to an exemplary sample x1 of class 21˜ . In addition, we present a more detailed traje…
Figure 6
Figure 6. Figure 6: Qualitative results (better viewed zoomed in). We visualize predictions on the SMILE-UHURA and the OCTA datasets for all [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Qualitative comparison of images generated by our [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 8
Figure 8. Figure 8: More trajectories of flow matching-based sampling. For [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]
Figure 10
Figure 10. Figure 10: Parametrization of our domain randomized generative pipeline. All parameters were carefully tuned to ensure sufficient [PITH_FULL_IMAGE:figures/full_fig_p013_10.png]
Figure 12
Figure 12. Figure 12: Masks contained in Msyn encompass a broad range of realistic vascular patterns, capturing variations in blood vessel scale, density, curvature, and tortuosity. I. Additional Qualitative Zero-Shot Results To emphasize the exceptional zero-shot generalization of vesselF…
Figure 11
Figure 11. Figure 11: More slices of exemplary domain randomized im [PITH_FULL_IMAGE:figures/full_fig_p015_11.png]
Figure 13
Figure 13. Figure 13: Qualitative results achieved on an exemplary test sample from the SMILE-UHURA dataset [ [PITH_FULL_IMAGE:figures/full_fig_p017_13.png]
Figure 14
Figure 14. Figure 14: Qualitative results achieved on multiple test samples from the MSD8 dataset [ [PITH_FULL_IMAGE:figures/full_fig_p017_14.png]
Figure 15
Figure 15. Figure 15: Qualitative results achieved on the three test volumes extracted from the BvEM dataset [ [PITH_FULL_IMAGE:figures/full_fig_p018_15.png]
Figure 16
Figure 16. Figure 16: Qualitative results achieved on two test samples from the OCTA dataset [ [PITH_FULL_IMAGE:figures/full_fig_p018_16.png]

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Forward citations

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Reference graph

Works this paper leans on

63 extracted references · 53 canonical work pages · cited by 2 Pith papers

  1. [1]

    VesselShot: Few-shot learning for cerebral blood vessel segmentation

    Mumu Aktar, Hassan Rivaz, Marta Kersten-Oertel, and Yim- ing Xiao. VesselShot: Few-shot learning for cerebral blood vessel segmentation. In International Workshop on Machine Learning in Clinical Neuroimaging, pages 46–55. Springer,

    work page

  2. [2]

    The Medical Segmentation Decathlon

    Michela Antonelli, Annika Reinke, Spyridon Bakas, Key- van Farahani, Annette Kopp-Schneider, Bennett A Landman, Geert Litjens, Bjoern Menze, Olaf Ronneberger, Ronald M Summers, et al. The Medical Segmentation Decathlon. Na- ture communications, 13(1):4128, 2022. 3, 6, 2, 5

    work page 2022

  3. [3]

    V oronoi diagrams—a survey of a fun- damental geometric data structure

    Franz Aurenhammer. V oronoi diagrams—a survey of a fun- damental geometric data structure. ACM Computing Surveys (CSUR), 23(3):345–405, 1991. 4

    work page 1991

  4. [4]

    SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining

    Benjamin Billot, Douglas N Greve, Oula Puonti, Axel Thielscher, Koen Van Leemput, Bruce Fischl, Adrian V Dalca, Juan Eugenio Iglesias, et al. SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining. Medical image analysis, 86:102789, 2023. 2, 4

    work page 2023

  5. [5]

    Leptomeningeal collaterals regulate reperfusion in ischemic stroke and rescue the brain from futile recanal- ization

    Nadine Felizitas Binder, Mohamad El Amki, Chaim Gl ¨uck, William Middleham, Anna Maria Reuss, Adrien Bertolo, Patrick Thurner, Thomas Deffieux, Chryso Lambride, Robert Epp, et al. Leptomeningeal collaterals regulate reperfusion in ischemic stroke and rescue the brain from futile recanal- ization. Neuron, 112(9):1456–1472, 2024. 3, 1

    work page 2024

  6. [6]

    IXI Dataset

    brain-development.org. IXI Dataset. http://brain- development.org/ixi-dataset/, n.d. 3, 5

    work page

  7. [7]

    Vessel Tortuosity and Brain Tumor Malignancy: A Blinded Study

    Elizabeth Bullitt, Donglin Zeng, Guido Gerig, Stephen Ayl- ward, Sarang Joshi, J Keith Smith, Weili Lin, and Matthew G Ewend. Vessel Tortuosity and Brain Tumor Malignancy: A Blinded Study. Academic radiology, 12(10):1232–1240,

    work page

  8. [8]

    Uni- verSeg: Universal Medical Image Segmentation

    Victor Ion Butoi, Jose Javier Gonzalez Ortiz, Tianyu Ma, Mert R Sabuncu, John Guttag, and Adrian V Dalca. Uni- verSeg: Universal Medical Image Segmentation. InProceed- ings of the IEEE/CVF International Conference on Com- puter Vision, pages 21438–21451, 2023. 3

    work page 2023

Show all 63 references
  1. [9]

    SMILE-UHURA Challenge–Small Vessel Seg- mentation at Mesoscopic Scale from Ultra-High Resolu- tion 7T Magnetic Resonance Angiograms

    Soumick Chatterjee, Hendrik Mattern, Marc D ¨orner, Alessandro Sciarra, Florian Dubost, Hannes Schnurre, Ru- pali Khatun, Chun-Chih Yu, Tsung-Lin Hsieh, Yi-Shan Tsai, et al. SMILE-UHURA Challenge–Small Vessel Seg- mentation at Mesoscopic Scale from Ultra-High Resolu- tion 7T M...

    2024 arXiv

  2. [10]

    Attention- Assisted Adversarial Model for Cerebrovascular Segmenta- tion in 3D TOF-MRA V olumes.IEEE Transactions on Med- ical Imaging, 41(12):3520–3532, 2022

    Ying Chen, Darui Jin, Bin Guo, and Xiangzhi Bai. Attention- Assisted Adversarial Model for Cerebrovascular Segmenta- tion in 3D TOF-MRA V olumes.IEEE Transactions on Med- ical Imaging, 41(12):3520–3532, 2022. 3, 5

    2022

  3. [11]

    Functional connectomics spanning multiple areas of mouse visual cortex

    MICrONS Consortium, J Alexander Bae, Mahaly Baptiste, Caitlyn A Bishop, Agnes L Bodor, Derrick Brittain, JoAnn Buchanan, Daniel J Bumbarger, Manuel A Castro, Brendan Celii, et al. Functional connectomics spanning multiple areas of mouse visual cortex. bioRxiv, pages 2021–07, 2021. 7

    2021

  4. [12]

    Automatic Segmentation, Feature Extraction and Comparison of Healthy and Stroke Cerebral Vasculature

    Aditi Deshpande, Nima Jamilpour, Bin Jiang, Patrik Michel, Ashraf Eskandari, Chelsea Kidwell, Max Wintermark, and Kaveh Laksari. Automatic Segmentation, Feature Extraction and Comparison of Healthy and Stroke Cerebral Vasculature. NeuroImage: Clinical, 30:102573, 2021. 1

    2021

  5. [13]

    AnyStar: Domain randomized universal star-convex 3D in- stance segmentation

    Neel Dey, Mazdak Abulnaga, Benjamin Billot, Esra Abaci Turk, Ellen Grant, Adrian V Dalca, and Polina Golland. AnyStar: Domain randomized universal star-convex 3D in- stance segmentation. In Proceedings of the IEEE/CVF Win- ter Conference on Applications of Computer Vision , pa...

    2024

  6. [14]

    Conditional Diffusion Models for Semantic 3D Brain MRI Synthesis

    Zolnamar Dorjsembe, Hsing-Kuo Pao, Sodtavilan Odonchimed, and Furen Xiao. Conditional Diffusion Models for Semantic 3D Brain MRI Synthesis. IEEE Journal of Biomedical and Health Informatics , 2024. 2, 5, 6, 8

    2024

  7. [15]

    Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

    Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas M ¨uller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling Rectified Flow Transformers for High-Resolution Image Synthesis. In Forty-first International Conference on Machin...

  8. [16]

    Deep Generative Models for 3D Medical Image Synthesis

    Paul Friedrich, Yannik Frisch, and Philippe C Cattin. Deep Generative Models for 3D Medical Image Synthesis. arXiv preprint arXiv:2410.17664, 2024. 2

    2024 arXiv

  9. [17]

    Bessel beam optical coherence microscopy enables multiscale as- sessment of cerebrovascular network morphology and func- tion

    Lukas Glandorf, Bastian Wittmann, Jeanne Droux, Chaim Gl¨uck, Bruno Weber, Susanne Wegener, Mohamad El Amki, Rainer Leitgeb, Bjoern Menze, and Daniel Razansky. Bessel beam optical coherence microscopy enables multiscale as- sessment of cerebrovascular network morphology and fu...

    2024

  10. [18]

    Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models

    Mohammad Haft-Javaherian, Linjing Fang, Victorine Muse, Chris B Schaffer, Nozomi Nishimura, and Mert R Sabuncu. Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models. PloS one, 14(3):e0213539, 2019. 3

    2019

  11. [19]

    GenerateCT: Text- Conditional Generation of 3D Chest CT V olumes

    Ibrahim Ethem Hamamci, Sezgin Er, Anjany Sekuboy- ina, Enis Simsar, Alperen Tezcan, Ayse Gulnihan Sim- sek, Sevval Nil Esirgun, Furkan Almas, Irem Dogan, Muhammed Furkan Dasdelen, et al. GenerateCT: Text- Conditional Generation of 3D Chest CT V olumes. arXiv preprint arXiv:230...

    2023 arXiv

  12. [20]

    Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

    Ali Hatamizadeh, Vishwesh Nath, Yucheng Tang, Dong Yang, Holger R Roth, and Daguang Xu. Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images. In International MICCAI brainlesion work- shop, pages 272–284. Springer, 2021. 8

    2021

  13. [21]

    UNETR: Transformers for 3D Med- ical Image Segmentation

    Ali Hatamizadeh, Yucheng Tang, Vishwesh Nath, Dong Yang, Andriy Myronenko, Bennett Landman, Holger R Roth, and Daguang Xu. UNETR: Transformers for 3D Med- ical Image Segmentation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages 574–5...

    2022

  14. [22]

    SwinUNETR-V2: Stronger Swin Transformers with Stagewise Convolutions for 3D Medical Image Segmentation

    Yufan He, Vishwesh Nath, Dong Yang, Yucheng Tang, Andriy Myronenko, and Daguang Xu. SwinUNETR-V2: Stronger Swin Transformers with Stagewise Convolutions for 3D Medical Image Segmentation. In International Conference on Medical Image Computing and Computer- Assisted Interventio...

    2023

  15. [23]

    VISTA3D: Versatile Imaging SegmenTation and Annotation model for 3D Computed To- mography

    Yufan He, Pengfei Guo, Yucheng Tang, Andriy Myronenko, Vishwesh Nath, Ziyue Xu, Dong Yang, Can Zhao, Benjamin Simon, Mason Belue, et al. VISTA3D: Versatile Imaging SegmenTation and Annotation model for 3D Computed To- mography. arXiv preprint arXiv:2406.05285, 2024. 2, 6, 3

    2024 arXiv

  16. [24]

    tUbe net: a generalisable deep learning tool for 3D vessel segmen- tation

    Natalie Holroyd, Zhongwang Li, Claire Walsh, Emmeline Brown, Rebecca Shipley, and Simon Walker-Samuel. tUbe net: a generalisable deep learning tool for 3D vessel segmen- tation. bioRxiv, pages 2023–07, 2023. 2, 3, 6, 1

    2023

  17. [25]

    Artifacts and artifact removal in optical coherence tomographic angiogra- phy

    Tristan T Hormel, David Huang, and Yali Jia. Artifacts and artifact removal in optical coherence tomographic angiogra- phy. Quantitative Imaging in Medicine and Surgery , 11(3): 1120, 2021. 7

    2021

  18. [26]

    nnU-Net: a self-configuring method for deep learning-based biomedical image segmen- tation

    Fabian Isensee, Paul F Jaeger, Simon AA Kohl, Jens Pe- tersen, and Klaus H Maier-Hein. nnU-Net: a self-configuring method for deep learning-based biomedical image segmen- tation. Nature methods, 18(2):203–211, 2021. 6, 2

    2021

  19. [27]

    Segment any- thing

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer White- head, Alexander C Berg, Wan-Yen Lo, et al. Segment any- thing. In Proceedings of the IEEE/CVF International Con- ference on Computer Vision, pages 4015–4026, 2023. 1, 2

    2023

  20. [28]

    Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models

    Nicholas Konz, Yuwen Chen, Haoyu Dong, and Maciej A Mazurowski. Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 88–98. Springer,

  21. [29]

    Terabyte-scale supervised 3D training and benchmarking dataset of the mouse kidney

    Willy Kuo, Diego Rossinelli, Georg Schulz, Roland H Wenger, Simone Hieber, Bert M ¨uller, and Vartan Kurtcuoglu. Terabyte-scale supervised 3D training and benchmarking dataset of the mouse kidney. Scientific Data, 10(1):510, 2023. 3, 1

    2023

  22. [30]

    Ho Hin Lee, Shunxing Bao, Yuankai Huo, and Bennett A. Landman. 3D UX-Net: A Large Kernel V olumetric Con- vNet Modernizing Hierarchical Transformer for Medical Im- age Segmentation. In The Eleventh International Conference on Learning Representations, 2023. 8

    2023

  23. [31]

    Blood vessel tail artifacts sup- pression in optical coherence tomography angiography.Neu- rophotonics, 9(2):021906–021906, 2022

    Yuntao Li and Jianbo Tang. Blood vessel tail artifacts sup- pression in optical coherence tomography angiography.Neu- rophotonics, 9(2):021906–021906, 2022. 7

    2022

  24. [32]

    Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maxim- ilian Nickel, and Matthew Le. Flow Matching for Genera- tive Modeling. In The Eleventh International Conference on Learning Representations, 2023. 3, 5

    2023

  25. [33]

    Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

    Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow. arXiv preprint arXiv:2209.03003 ,

    arXiv

  26. [34]

    A ConvNet for the 2020s

    Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feicht- enhofer, Trevor Darrell, and Saining Xie. A ConvNet for the 2020s. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition , pages 11976–11986,

  27. [35]

    Segment anything in medical images

    Jun Ma, Yuting He, Feifei Li, Lin Han, Chenyu You, and Bo Wang. Segment anything in medical images. Nature Communications, 15(1):654, 2024. 1, 2, 3

    2024

  28. [36]

    SiT: Exploring Flow and Diffusion-Based Generative Models with Scalable Interpolant Transformers

    Nanye Ma, Mark Goldstein, Michael S Albergo, Nicholas M Boffi, Eric Vanden-Eijnden, and Saining Xie. SiT: Exploring Flow and Diffusion-Based Generative Models with Scalable Interpolant Transformers. In European Conference on Com- puter Vision, pages 23–40, 2024. 3, 5

    2024

  29. [37]

    Deep learning-based cerebral aneurysm segmentation and morphological analy- sis with three-dimensional rotational angiography

    Hidehisa Nishi, Nicole M Cancelliere, Ariana Rustici, Guil- laume Charbonnier, Vanessa Chan, Julian Spears, Thomas R Marotta, and Vitor Mendes Pereira. Deep learning-based cerebral aneurysm segmentation and morphological analy- sis with three-dimensional rotational angiography...

    2024

  30. [38]

    An image synthesizer

    Ken Perlin. An image synthesizer. ACM Siggraph Computer Graphics, 19(3):287–296, 1985. 4

    1985

  31. [39]

    Automated lung vessel segmentation reveals blood vessel volume redistribu- tion in viral pneumonia

    Julien Poletti, Michael Bach, Shan Yang, Raphael Sexauer, Bram Stieltjes, David C Rotzinger, Jens Bremerich, Alexan- der Walter Sauter, and Thomas Weikert. Automated lung vessel segmentation reveals blood vessel volume redistribu- tion in viral pneumonia. European Journal of R...

    2022

  32. [40]

    A dataset of rodent cerebrovasculature from in vivo multiphoton fluores- cence microscopy imaging

    Charissa Poon, Petteri Teikari, Muhammad Febrian Rach- madi, Henrik Skibbe, and Kullervo Hynynen. A dataset of rodent cerebrovasculature from in vivo multiphoton fluores- cence microscopy imaging. Scientific Data, 10(1):141, 2023. 3

    2023

  33. [41]

    SAM 2: Segment Anything in Images and Videos

    Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman R¨adle, Chloe Rolland, Laura Gustafson, et al. SAM 2: Segment Anything in Images and Videos. arXiv preprint arXiv:2408.00714, 2024. 2

    2024 arXiv

  34. [42]

    MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation

    Saikat Roy, Gregor Koehler, Constantin Ulrich, Michael Baumgartner, Jens Petersen, Fabian Isensee, Paul F Jaeger, and Klaus H Maier-Hein. MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation. In International Conference on Medical Image Computing and ...

  35. [43]

    clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation

    Suprosanna Shit, Johannes C Paetzold, Anjany Sekuboyina, Ivan Ezhov, Alexander Unger, Andrey Zhylka, Josien PW Pluim, Ulrich Bauer, and Bjoern H Menze. clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation. In Proceedings of the IEEE/CVF Confere...

    2021

  36. [44]

    3D image reconstruc- tion for comparison of algorithm database: A patient spe- cific anatomical and medical image database

    Luc Soler, Alexandre Hostettler, Vincent Agnus, Arnaud Charnoz, J Fasquel, Johan Moreau, A Osswald, Mourad Bouhadjar, and Jacques Marescaux. 3D image reconstruc- tion for comparison of algorithm database: A patient spe- cific anatomical and medical image database. IRCAD, Stras...

    2010

  37. [45]

    Rapid and fully automated blood vasculature analysis in 3D light-sheet image volumes of different organs

    Philippa Spangenberg, Nina Hagemann, Anthony Squire, Nils F ¨orster, Sascha D Krauß, Yachao Qi, Ayan Mohamud Yusuf, Jing Wang, Anika Gr¨uneboom, Lennart Kowitz, et al. Rapid and fully automated blood vasculature analysis in 3D light-sheet image volumes of different organs. Cel...

    2023

  38. [46]

    DeepVesselNet: Ves- sel Segmentation, Centerline Prediction, and Bifurcation De- tection in 3-D Angiographic V olumes

    Giles Tetteh, Velizar Efremov, Nils D Forkert, Matthias Schneider, Jan Kirschke, Bruno Weber, Claus Zimmer, 10 Marie Piraud, and Bj ¨orn H Menze. DeepVesselNet: Ves- sel Segmentation, Centerline Prediction, and Bifurcation De- tection in 3-D Angiographic V olumes. Frontiers in...

    2020

  39. [47]

    Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World

    Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Woj- ciech Zaremba, and Pieter Abbeel. Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World. In IEEE/RSJ international conference on intelligent robots and systems (IROS) , pages 23–30. IEEE,

  40. [48]

    Machine learning analysis of whole mouse brain vasculature

    Mihail Ivilinov Todorov, Johannes Christian Paetzold, Oliver Schoppe, Giles Tetteh, Suprosanna Shit, Velizar Efremov, Katalin Todorov-V ¨olgyi, Marco D ¨uring, Martin Dichgans, Marie Piraud, et al. Machine learning analysis of whole mouse brain vasculature. Nature methods, 17(...

  41. [49]

    Hierarchical imaging and computational analysis of three-dimensional vascular network architecture in the entire postnatal and adult mouse brain

    Thomas W ¨alchli, Jeroen Bisschop, Arttu Miettinen, Alexan- dra Ulmann-Schuler, Christoph Hinterm ¨uller, Eric P Meyer, Thomas Krucker, Regula W ¨alchli, Philippe P Monnier, Pe- ter Carmeliet, et al. Hierarchical imaging and computational analysis of three-dimensional vascular...

    2021

  42. [50]

    Near-lifespan longitudinal tracking of brain microvascular morphology, topology, and flow in male mice

    Konrad W Walek, Sabina Stefan, Jang-Hoon Lee, Pooja Put- tigampala, Anna H Kim, Seong Wook Park, Paul J Marc- hand, Frederic Lesage, Tao Liu, Yu-Wen Alvin Huang, et al. Near-lifespan longitudinal tracking of brain microvascular morphology, topology, and flow in male mice. Natu...

    2023

  43. [51]

    TriSAM: Tri-Plane SAM for zero-shot cortical blood vessel segmentation in VEM images

    Jia Wan, Wanhua Li, Atmadeep Banerjee, Jason Ken Ad- hinarta, Evelina Sjostedt, Jingpeng Wu, Jeff Lichtman, Hanspeter Pfister, and Donglai Wei. TriSAM: Tri-Plane SAM for zero-shot cortical blood vessel segmentation in VEM images. arXiv preprint arXiv:2401.13961 , 2024. 2, 3, 6...

    2024 arXiv

  44. [52]

    SAM-OCTA: A Fine-Tuning Strategy for Apply- ing Foundation Model OCTA Image Segmentation Tasks

    Chengliang Wang, Xinrun Chen, Haojian Ning, and Shiy- ing Li. SAM-OCTA: A Fine-Tuning Strategy for Apply- ing Foundation Model OCTA Image Segmentation Tasks. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages 1771–1775...

    2024

  45. [53]

    SAM-Med3D: Towards General-purpose Seg- mentation Models for V olumetric Medical Images

    Haoyu Wang, Sizheng Guo, Jin Ye, Zhongying Deng, Jun- long Cheng, Tianbin Li, Jianpin Chen, Yanzhou Su, Ziyan Huang, Yiqing Shen, Bin Fu, Shaoting Zhang, Junjun He, and Yu Qiao. SAM-Med3D: Towards General-purpose Seg- mentation Models for V olumetric Medical Images. arXiv prep...

    arXiv

  46. [54]

    Simulation-Based Segmentation of Blood Vessels in Cerebral 3D OCTA Images

    Bastian Wittmann, Lukas Glandorf, Johannes C Paetzold, Tamaz Amiranashvili, Thomas W ¨alchli, Daniel Razansky, and Bjoern Menze. Simulation-Based Segmentation of Blood Vessels in Cerebral 3D OCTA Images. In In- ternational Conference on Medical Image Computing and Computer-Ass...

  47. [55]

    Link Prediction for Flow-Driven Spatial Networks

    Bastian Wittmann, Johannes C Paetzold, Chinmay Prab- hakar, Daniel Rueckert, and Bjoern Menze. Link Prediction for Flow-Driven Spatial Networks. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2472–2481, 2024. 8

    2024

  48. [56]

    Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

    Junde Wu, Wei Ji, Yuanpei Liu, Huazhu Fu, Min Xu, Yanwu Xu, and Yueming Jin. Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation. arXiv preprint arXiv:2304.12620, 2023. 2

    2023 arXiv

  49. [57]

    Main Coronary Vessel Segmentation Using Deep Learning in Smart Medical.Math- ematical Problems in Engineering, 2020(1):8858344, 2020

    Zhanchao Xian, Xiaoqing Wang, Shaodi Yan, Dahao Yang, Junyu Chen, and Changnong Peng. Main Coronary Vessel Segmentation Using Deep Learning in Smart Medical.Math- ematical Problems in Engineering, 2020(1):8858344, 2020. 1

    2020

  50. [58]

    Deep Learning for Vascular Segmentation and Applications in Phase Contrast Tomography Imaging

    Ekin Yagis, Shahab Aslani, Yashvardhan Jain, Yang Zhou, Shahrokh Rahmani, Joseph Brunet, Alexandre Bel- lier, Christopher Werlein, Maximilian Ackermann, Danny Jonigk, et al. Deep Learning for Vascular Segmentation and Applications in Phase Contrast Tomography Imaging. arXiv pr...

    2023 arXiv

  51. [59]

    Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA.arXiv preprint arXiv:2312.17670,

    Kaiyuan Yang, Fabio Musio, Yihui Ma, Norman Juchler, Jo- hannes C Paetzold, Rami Al-Maskari, Luciano H¨oher, Hong- wei Bran Li, Ibrahim Ethem Hamamci, Anjany Sekuboyina, et al. Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle...

    arXiv

  52. [60]

    Visibility of microvessels in Optical Coherence Tomography angiography depends on angular orientation

    Jun Zhu, Marcel T Bernucci, Conrad W Merkle, and Vivek J Srinivasan. Visibility of microvessels in Optical Coherence Tomography angiography depends on angular orientation. Journal of biophotonics, 13(10):e202000090, 2020. 7

    2020

  53. [61]

    Medical SAM 2: Seg- ment medical images as video via Segment Anything Model

    Jiayuan Zhu, Yunli Qi, and Junde Wu. Medical SAM 2: Seg- ment medical images as video via Segment Anything Model

  54. [62]

    1, 2, 6, 3

    arXiv preprint arXiv:2408.00874, 2024. 1, 2, 6, 3

    2024 arXiv

  55. [63]

    Dr-SAM: An End-to-End Framework for Vascular Seg- mentation Diameter Estimation and Anomaly Detection on Angiography Images

    Vazgen Zohranyan, Vagner Navasardyan, Hayk Navasardyan, Jan Borggrefe, and Shant Navasardyan. Dr-SAM: An End-to-End Framework for Vascular Seg- mentation Diameter Estimation and Anomaly Detection on Angiography Images. In Proceedings of the IEEE/CVF Conference on Computer Visi...

    2024

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.