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REVIEW 3 major objections 5 minor 56 references

The R-Vessel-X Project

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This report claims the R-Vessel-X project delivered a complete, open-source pipeline for analyzing and simulating liver vasculature in 3D medical images.

desk verdict A candid project summary with no new science; its value depends entirely on whether the linked repos and cited papers actually support the claims, especially the clinical-use and speed claims. read the letter →

arxiv 2501.10068 v1 pith:GXLBXTE2 submitted 2025-01-17 eess.IV

Abir Affane (IP) , Mohamed Amine Chetoui (IP) , Jonas Lamy (LIRIS) , Guillaume Lienemann (IP) , Raphaël Peron (IP) , P. Beaurepaire (IP) , Guillaume Dollé (LMR) , Marie-Ange Lèbre (IP)
show 11 more authors
Benoit Magnin (IP) Odyssée Merveille (CREATIS) Mathilde Morvan (IP) Phuc Ngo (LORIA) Thibault Pelletier Hugo Rositi (LORIA) Stéphanie Salmon (LMR) Julien Finet Bertrand Kerautret (LIRIS) Nicolas Passat (CRESTIC) Antoine Vacavant (IP)
This is my paper · ORCID
classification eess.IV
keywords livervesselsegmentationvesselnessfiltersangiographicimageanalysisconstrainedconstructiveoptimizationhepaticperfusionsimulationopen-sourcesoftware3Dmedicalimagingdeeplearning
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 technical report is a summary of the four-year research project R-Vessel-X, which aimed to develop robust methods for extracting and understanding vascular networks in three-dimensional liver images. The paper claims that the project produced an interlinked suite of outcomes covering the full analysis pipeline: a reproducible benchmark for vesselness filters, deep-learning models for liver vessel segmentation that benefit from vesselness pre-filtering, an open-source implementation of a constructive optimization algorithm for generating realistic vascular trees, and a numerical pipeline for simulating hepatic perfusion. It also reports the release of two plug-ins for interactive liver anatomy segmentation and vesselness filtering, plus an online demonstrator for vessel generation. The stated value is that all methods and software are open-source and publicly available, so other researchers and clinicians can use them without proprietary datasets or tools.

What carries the argument

The central objects are: (1) vesselness filters, operators that enhance tubular structures in images, whose benchmark is the project's filtering contribution; (2) 3D Dense U-Net, a deep convolutional segmentation architecture with dense connections, trained on filter-enhanced images; (3) Constrained Constructive Optimization (CCO), an algorithm that incrementally builds a binary tree of vessel segments by optimizing terminal point placements subject to physiological rules; and (4) a multi-compartment Darcy/CFD coupling for computing perfusion parameters. The filter benchmark provides the machinery that ties the pieces together: it yields reliable filter implementations, and the filters in turn provide vascular pattern inputs that improve segmentation and can synthesize training data.

What would settle it

Download the three referenced public repositories and run the described workflows: re-run the filter benchmark on the same public datasets and check that the reported mean MCC/Dice table reproduces; train the Dense U-Net with and without the Jerman filter on the same data and check the Dice gain; and verify that the liver segmentation plugin is functional and used at the stated hospital. If any of these fail, the project's stated outcomes as summarized here are not reproducible.

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Extended reading notes

Core claim

The paper's central claim is that the R-Vessel-X project delivered extensive research outcomes that collectively cover filtering, segmentation, modeling, and simulation for 3D angiographic image analysis, with a focus on the liver. On the segmentation side, it reports that combining a vesselness filter as preprocessing with a 3D Dense U-Net raises liver vessel segmentation Dice scores from 0.671 to 0.856 on a public liver CT dataset, while preserving bifurcations and small vessels. On the modeling side, it presents an open-source revisitation of the Constrained Constructive Optimization algorithm that generates realistic synthetic vascular trees, including the liver vasculature when seeded with image-extracted major branches. The simulation work couples Darcy flow and computational fluid dynamics in a multi-compartment model to compute pressure, flow, and permeability in the liver parenchyma and vessels. The paper frames these results as reproducible contributions, with permanent links to the software and an emphasis on open diffusion to the biomedical engineering community.

Load-bearing premise

The report assumes that the cited publications and public repositories actually contain the methods, results, and clinical usage that the summary describes, since none of those sources is reproduced or audited in this paper.

Editorial extensions

If this is right

  • Any research group can run the same filtering benchmark on its own volumes and compare new operators against seven standard filters under controlled parameters.
  • The reported Dice gains suggest that vesselness pre-filtering is a cheap, effective way to improve deep vessel segmentation, especially for small vessels and bifurcations.
  • OpenCCO gives the community a free, open alternative to closed implementations of CCO for generating patient- or organ-constrained synthetic vascular trees.
  • The simulation pipeline can turn an image-derived liver vascular tree plus organ boundary into computed physiological maps of pressure and flow, useful for pre-surgical planning.
  • The plug-ins put the project's methods directly into a commonly used medical image analysis environment, lowering the barrier to clinical and research adoption.

Reading between the lines

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

  • The report itself contains no experiments; every substantive claim is deferred to the cited publications, so the summary should be read as a project overview rather than as new evidence.
  • If the filter benchmark and OpenCCO are widely adopted, a fully synthetic training pipeline becomes practical: generate vessels, render them, filter them, and train segmentation models with known ground truth.
  • The stronger clinical claims (faster annotation than commercial tools; use in a radiology department) are asserted from other sources and would need a direct comparative study to be verified.
  • The success of filter-enhanced deep segmentation suggests a testable extension: on datasets with different contrast or modality (e.g., MRI), the optimal filter might differ, and the benchmark is the right tool to find it.
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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. This technical report summarizes the ANR-funded R-Vessel-X project (2019-2023) on hepatic vascular image analysis. It reports contributions in four areas: a benchmarking framework and C++ library for vesselness filtering (§2.1), deep-learning liver vessel segmentation (§2.2), the OpenCCO tool for CCO-based vascular tree generation (§2.3), a hepatic perfusion simulation pipeline (§2.4), and two 3D Slicer plug-ins (§2.5). The paper concludes that the project delivered extensive research outcomes and that one plug-in is used in clinical practice. Most technical details and quantitative results are delegated to cited publications; the manuscript itself is a high-level project overview.

Significance. If the cited works and repositories contain what the summary claims, the project has produced useful open-source resources: a filter benchmark on public datasets, 3D Slicer plug-ins with permanent links, an IPOL demo, and reproducible simulation code. The strength of the report is its explicit indexing of these artifacts, including public dataset usage and JOSS/IPOL citations. However, the manuscript presents no new method or data, and its significance therefore depends entirely on external sources the reader cannot audit from the text alone.

major comments (3)
  1. [§2.5 and §3] The claim that SlicerRVXLiverSegmentation enables "a faster annotation compared to commercial solutions, mostly for MRI data" and is "used in the radiology department at CHU of Clermont-Ferrand" is load-bearing for the project's impact statement, but no direct evidence is provided in this manuscript; the sole support is a citation to Lamy et al. (2022b). A JOSS paper is a software-description venue and does not by itself document a comparative timing study or clinical deployment. Please include the supporting measurements, reproduce the relevant comparisons, or clearly attribute these statements as claims of the cited external paper and qualify the definitive wording accordingly.
  2. [§2.5] The sentence "this is the very first time that such plug-in incorporates deep models based on MONAI for automatic liver volume segmentation" is an unsupported priority claim. No prior-art search or citation is given, and the phrasing "such plug-in" is ambiguous about the comparison class (3D Slicer plug-ins? liver-segmentation plug-ins? MONAI-based plug-ins?). Either provide a precise, verifiable basis for the claim or remove it.
  3. [Abstract, §2.1–§2.5, §3] The paper's central assertion—that the project "provided extensive research outcomes" and delivered the described tools—is not directly auditable from this manuscript. All quantitative results (Tables 1 and 2) are stated to come from external publications, and the software artifacts are only described through links and citations. As a project report this is acceptable, but the authors should add a short "availability and validation" subsection reporting the repository versions, installability status, and any basic reproducibility check (e.g., a CI badge or container definition), so that the deliverables can be verified independently of the project's own papers.
minor comments (5)
  1. [Abstract] The abstract contains a spacing typo, "toobtain", which should read "to obtain".
  2. [§2.5] The phrase "we shew" is an archaic or typographical form; it should be "we showed".
  3. [References] In the Kikinis et al. (2014) reference, "V osburgh" contains an erroneous space and should be "Vosburgh".
  4. [§2.1] The text describes "seven gold standard filters", and Table 1 lists Baseline plus seven methods; the caption and the table would benefit from explicitly stating that Baseline is a comparison method and not one of the seven filtering operators.
  5. [Acknowledgement] The acknowledgement writes "AgenceNationale de la Recherche" without a space, while the footnote in the abstract spells it correctly; please unify the spelling.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the report is a self-referential project summary, but its central deliverables are externally checkable and no derivation reduces to its own inputs.

full rationale

This is a technical report summarizing a funded project, not a derivation chain with equations. The contributions (vesselness filter benchmark, deep segmentation models, OpenCCO generator, perfusion simulation, 3D Slicer plug-ins) are presented as outcomes and are delegated to the project's own publications, theses, GitHub repositories, and an IPOL demo. This makes the report heavily self-referential: the evidence for the central claim of 'extensive research outcomes' consists largely of the project's own outputs. However, the existence of those outputs is independently checkable through the provided permanent links (e.g., github.com/R-Vessel-X/SlicerRVXVesselnessFilters, github.com/R-Vessel-X/SlicerRVXLiverSegmentation, and the OpenCCO IPOL demo), so the central claim does not reduce to the report's assumptions. Two specific claims are not auditable within this paper and are flagged as verifiability weaknesses rather than circularity: Section 2.5 asserts that SlicerRVXLiverSegmentation enables 'a faster annotation compared to commercial solutions, mostly for MRI data' based solely on the authors' own JOSS paper (Lamy et al., 2022b), and Section 3 asserts that the plug-in 'is used in the radiology department at CHU of Clermont-Ferrand' with no supporting evidence. These are load-bearing statements about clinical utility and adoption, but they are not derivations, fitted predictions, or imported uniqueness theorems; removing them does not make the rest of the report circular. No equation is defined in terms of the quantity it claims to predict, and no fitted parameter is renamed as a prediction.

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

The central claim is that project outputs exist and are open-source; this rests on external artifacts and prior papers rather than on new derivations. No free parameters are introduced. The domain assumptions listed are background modeling choices inherited from the cited literature.

assumptions (3)
  • domain assumption Vesselness filters provide useful preprocessing for hepatic vessel segmentation.
    Section 2.2 interprets Dice gains with Jerman pre-filtering as a meaningful result; this relies on the broader validity of vesselness enhancement for this task.
  • domain assumption Constrained Constructive Optimization generates physiologically plausible vascular trees.
    Section 2.4 uses OpenCCO-generated vessels as the basis for liver perfusion simulation without validating the synthetic anatomy against clinical ground truth.
  • domain assumption Multi-compartment Darcy flow coupled with CFD represents hepatic perfusion.
    Section 2.4 states the simulation approach and cites Rohan et al. 2016; the report does not independently validate this model.

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Cite this review

Pith. "Pith review of The R-Vessel-X Project." pith.science (2026). https://pith.science/paper/GXLBXTE2

@misc{pith2026250110068,
  author       = {Pith},
  title        = {Pith review of: The R-Vessel-X Project},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GXLBXTE2}},
  note         = {Machine review of arXiv:2501.10068}
}
read the original abstract

1) Objectives: This technical report presents a synthetic summary and the principal outcomes of the project R-Vessel-X ("Robust vascular network extraction and understanding within hepatic biomedical images") funded by the French Agence Nationale de la Recherche, and developed between 2019 and 2023. 2) Material and methods: We used datasets and tools publicly available such as IRCAD, Bullitt or VascuSynth toobtain real or synthetic angiographic images. The main contributions lie in the field of 3D angiographic image analysis: filtering, segmentation, modeling and simulation, with a specific focus on the liver. 3) Results: We paid a particular attention to open-source software diffusion of the developed methods, by means of 3D Slicer plugins for the liver anatomy segmentation (SlicerRVXLiverSegmentation) and vesselness filtering (Slicer-RVXVesselnessFilters), and an online demo for the generation of synthetic and realistic vessels in 2D and 3D (OpenCCO). 4) Conclusion: The R-Vessel-X project provided extensive research outcomes, covering various topics related to 3D angiographic image analysis, such as filtering, segmentation, modeling and simulation. We also developed open-source and free softwares so that the research communities in biomedical engineering can use these results in their future research.

Figures

Figures reproduced from arXiv: 2501.10068 by the authors.

Figure 1
Figure 1. Global flowchart and organization of the R-Vessel-X project. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. 2. Contributions 2.1. Angiographic Image Filtering Filtering with the purpose of denoising and/or enhancing the vascular signal vs. the remaining patterns has been one of the first research topics in angiographic image analysis. Over the years, a wide range of filters were proposed, from derivative￾based (Frangi et al., 1998) to non-linear approaches (Merveille et al., 2018). Despite a large choice of operators, fil… view at source ↗
Figure 2
Figure 2. Biomedical engineering workflow for angiographic image analysis, and associated organization of the paper. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figures from the paper (5 more)
Figure 3
Figure 3. Figure 3: Typical output of the benchmark tools module illustrating numerical results obtained for a set of parameters and di [PITH_FULL_IMAGE:figures/full_fig_p003_3.png]
Figure 6
Figure 6. Figure 6: Generation of heart vascular network with OpenCCO, by taking [PITH_FULL_IMAGE:figures/full_fig_p004_6.png]
Figure 4
Figure 4. Figure 4: MIP visualisation of the filtering results on a bifurcation area of the [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Illustration of liver segmentation obtained with pre-filtered CT im [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]
Figure 7
Figure 7. Figure 7: Generation of liver vessels by means of OpenCCO (a), considering branches of portal and hepatic veins as initialization of the algorithm. Compu [PITH_FULL_IMAGE:figures/full_fig_p005_7.png]

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    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.