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REVIEW 3 major objections 6 minor 80 references

Vessel segmentation for X-separation

T0 review · 3 major / 6 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read A three-step, geometry-guided vessel segmentation pipeline outperforms Frangi and GRE-based methods on χ-separation maps, with the highest Dice scores.

desk verdict Solid vessel segmentation adaptation for chi-separation, but the reported Dice advantage is weakened by per-subject threshold tuning on the evaluation subjects. read the letter →

arxiv 2502.01023 v1 pith:LJRIUTCR submitted 2025-02-03 cs.CV q-bio.QM

Taechang Kim , Sooyeon Ji , Kyeongseon Min , Minjun Kim , Jonghyo Youn , Chungseok Oh , Jiye Kim , Jongho Lee This is my paper · ORCID
classification cs.CVq-bio.QM
keywords χ-separationvesselsegmentationquantitativesusceptibilitymapping(QSM)MFATvesselnessanisotropy-basedcleanupregiongrowingironandmyelinROIanalysis
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

$\chi$-separation produces paramagnetic and diamagnetic susceptibility maps intended to reflect iron and myelin in the brain, but blood vessels generate artifacts that distort local values. This paper aims to establish that a dedicated vessel segmentation step, built from the same maps that $\chi$-separation already produces, can remove vessels while sparing deep gray matter and myelinated fiber tracts. The proposed three-step pipeline seeds from high-pass-filtered $R_2^*$ and from the product of the two susceptibility maps, grows regions along vessel geometry using direction, intensity, and anisotropy criteria, and deletes connected components whose mean anisotropy is too low to be vessels. The paper reports that this mask outperforms Frangi and GRE-based vessel segmentation in Dice score, improves error metrics when evaluating a neural-network $\chi$-separation reconstruction, and changes population-averaged ROI susceptibility values significantly in 16 of 27 tested regions.

What carries the argument

The load-bearing object is the anisotropy measure $Ani(q)=|\lambda_2(q)\lambda_3(q)|$ formed from the two larger-magnitude Hessian eigenvalues of the susceptibility map; it is used as a soft term inside the region-growing acceptance rule (Eq. 3) and as a per-connected-component mean threshold (Eq. 7) that removes non-vessel structures. The seed generation relies on an inverse Hamming high-pass filter on $R_2^*$ for large vessels and a maximum-intensity projection of $\chi_{\mathrm{para}}\cdot|\chi_{\mathrm{dia}}|$ for small vessels, both feeding the multi-scale fractional anisotropy tensor (MFAT) vesselness map, a Hessian-eigenvalue measure of how tubular a voxel is. The region-growing step then follows vessel direction by comparing the principal Hessian eigenvectors of neighbouring voxels while requiring intensity coherence and anisotropy, which together stop the mask from bleeding into bright but non-tubular structures.

What would settle it

Compute the per-connected-component anisotropy of Eq. 7 for manually labelled vessels, globus pallidus, and optic radiation across many subjects; if the vessel distribution overlaps substantially with the other two, no single threshold can separate them and the reported Dice advantage will not generalize. A complementary test is to run the pipeline on images containing confirmed calcifications, which the paper itself notes can mimic vessels by being hyperintense and tube-like.

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Extended reading notes

Core claim

The central discovery is that vessel segmentation for $\chi$-separation can be treated as a geometric filtering problem rather than a pure intensity-thresholding problem. Vessels are seeded from two complementary signals—large vessels from a high-pass-filtered $R_2^*$ map and small vessels from a maximum-intensity projection of $\chi_{\mathrm{para}}\cdot|\chi_{\mathrm{dia}}|$—then propagated by a region-growing rule that requires directionality similarity to the seed's Hessian eigenvector, intensity similarity, and a vesselness-anisotropy term. The final cleanup uses the mean of $|\lambda_2\lambda_3|$ over each connected component (Eq. 7) to discard non-vessel structures such as globus pallidus and optic radiation. In the reported experiments the method achieves Dice scores of $76.7\pm4.2\%$ ($\chi_{\mathrm{para}}$) and $68.7\pm7.9\%$ ($|\chi_{\mathrm{dia}}|$) at 3T and $76.9\pm2.7\%$ and $72.6\pm5.7\%$ at 7T, all above the two comparison methods, and it stays within a few points of its best score across four different $\chi$-separation algorithms.

Load-bearing premise

The method assumes that vessels are the only structures whose connected components are both bright in the seed maps and highly anisotropic, so that the Eq. 7 anisotropy threshold can delete globus pallidus and optic radiation without also deleting small vessels.

Editorial extensions

If this is right

  • Vessel masking becomes a practical preprocessing step for $\chi$-separation studies, since it changes reconstruction-quality metrics and ROI means enough to alter conclusions.
  • The reported Dice advantage implies that combining geometry-guided region growing with anisotropy cleanup captures more true vessels and fewer false positives than Hessian-only or multi-contrast heuristic segmentation.
  • The mask's stability across $\chi$-sep-COSMOS, $\chi$-sep-MEDI, $\chi$-sep-iLSQR, and $\chi$-sepnet-$R_2^*$ means a single segmentation protocol can be used regardless of which reconstruction algorithm generated the maps.
  • The modest resource footprint (2 GB RAM and 4 minutes on 3T data) makes the method practical for large-cohort analyses such as the 106-subject template study reported here.
  • Significant ROI shifts in 16 of 27 regions, including caudate and genu of corpus callosum, imply that previously reported atlas values without vessel masking may be biased in vessel-rich structures.

Reading between the lines

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

  • The anisotropy threshold in Eq. 7 is the pivot on which the method stands; a natural next experiment is to measure the anisotropy distributions of vessels versus globus pallidus and optic radiation across a large population to see how cleanly the two can be separated.
  • Because the inputs are generic susceptibility-style maps, the same seed-and-grow machinery could transfer to QSM or SWI vessel segmentation; the paper names this as future work, but it is not demonstrated here.
  • The masks produced with individually tuned hyperparameters could serve as training labels for a deep segmentation network, potentially removing the per-subject tuning the paper reports, though such a network is not tested in this work.
  • A testable extension is to validate the pipeline against whole-brain manual labels, since the current manual ground truth covers 12 central slices in three planes per subject rather than the full volume.
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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 / 6 minor

Summary. The paper presents a three-step vessel segmentation pipeline for chi-separation maps: (1) seed generation from R2* and the product of chi_para and |chi_dia| using MFAT filtering and MIP-based back-projection, (2) vessel-geometry-guided region growing with intensity limits and novel directional/intensity/anisotropy criteria, and (3) removal of non-vessel structures by connected-component anisotropy thresholding (Eq. 7). The method is evaluated against Frangi filter and a GRE-based vein-segmentation method on six manually labeled subjects (three 3T, three 7T) and across four chi-separation algorithms, reporting higher Dice scores (Tables 1 and 2). Two demonstrations are provided: improved RMSE/PSNR/SSIM when evaluating chi-sepnet-R2* against chi-sep-COSMOS after vessel masking (Table 3), and statistically significant effects of vessel exclusion in population-averaged ROI analysis of 106 subjects (Table 4). Code is made available as part of the chi-separation toolbox.

Significance. If the reported performance holds, this is practically valuable: it addresses a known artifact source in chi-separation analysis and provides a tool with modest computational cost and public code. The idea of using the product of chi_para and |chi_dia| for small-vessel seeds and the anisotropy-based connected-component refinement to suppress globus pallidus and optic radiation is sensible and clearly described. The paper also contributes two application-level validations (reconstruction-quality evaluation and ROI analysis), which increase the utility of the method beyond a purely algorithmic comparison. The central claims are supported by consistent DSC improvements, but the evaluation design has issues that need addressing before the superiority claim is fully convincing.

major comments (3)
  1. [Section 2.2, Eq. (7); Section 4 (Discussion)] The anisotropy threshold Aniso_Thresh was adjusted for each subject, starting from 1.2e-3 and increased when deep gray matter regions were not properly excluded. Because the DSC values in Tables 1 and 2 are computed on the same subjects used for this per-subject adjustment, the proposed method is not being compared under the same 'out-of-the-box' conditions as the Frangi filter (whose parameters were optimized per dataset via ROC) or the GRE-based method (which has no adjustable parameters). The reported threshold range spans nearly an order of magnitude (0.0012-0.0108 for chi_para with MEDI/iLSQR), indicating that the anisotropy measure is not automatically stable. Please report DSC with a single fixed threshold (e.g., the starting value) for all subjects, or use a cross-validation/held-out protocol, and provide a sensitivity analysis of the threshold. Without this, the claim of 'highest Dice' may be inflated by peaking.
  2. [Section 2.3, Supplementary Figure 3] The manual ground-truth masks explicitly exclude calcifications, meninges, and artifacts caused by mis-registration between R2* and R2. These are precisely among the structures that the proposed method is designed to exclude as non-vessel (the paper acknowledges calcification and meninges as failure modes in the Discussion). Excluding them from the labeled voxels removes known difficult cases from the Dice computation, which may overstate the method's ability to 'effectively exclude non-vessel structures' (Abstract, Conclusion). Please justify this exclusion or evaluate on a subset that includes these structures; at minimum, quantify the number and spatial extent of these excluded regions and discuss how their inclusion would affect DSC.
  3. [Section 2.4, Table 2; Section 4 (Discussion)] The robustness claim across four chi-separation algorithms is weakened by per-algorithm threshold adjustment. The Discussion states that higher Aniso_Thresh values were needed for chi-sep-MEDI and chi-sep-iLSQR maps (0.0072-0.0108 for chi_para) and that this may have led to exclusion of small vessels and slightly reduced DSC. With the threshold varied per algorithm and per subject, the similar DSCs in Table 2 partly reflect this tuning rather than automatic robustness. Please report the DSC obtained with a single fixed threshold (e.g., 1.2e-3) applied to all algorithms, and show how the threshold would need to be chosen in practice for a new dataset.
minor comments (6)
  1. [Eq. (3)] The vesselness vMFAT in Eq. (3) is stated to be computed from the susceptibility map, but in Step 1 vMFAT is computed from R2* and from chi_para * |chi_dia|. Please clarify explicitly which input image is used for the vMFAT entering Eq. (3) for each of the two masks (chi_para and |chi_dia|).
  2. [Eq. (7)] The notation for anisotropy is inconsistent: Eq. (6) defines Ani(q) as a per-voxel value, while Eq. (7) computes the mean over a connected component. A subscript such as Ani_CC or an explicit definition would improve readability.
  3. [Table 4] For some ROIs (e.g., red nucleus, subthalamic nucleus) the p-value is reported as '-' with no explanation. Please state why no p-value is given, presumably because the vessel proportion is zero or the paired difference has zero variance.
  4. [Supplementary Figure 5] The ROC curves for Frangi filter parameter optimization are not fully described: it is unclear which parameter was swept and what the 'optimum' criterion means in terms of the ROC operating point. Please define the swept parameter(s) and the selection rule.
  5. [Abstract and throughout] The phrase 'highest Dice score coefficient' should be 'highest Dice similarity coefficient' for consistency with the standard terminology.
  6. [Data availability] The code is said to be available as part of the chi-separation toolbox, but no version or specific repository path is given. Please provide a stable link or a DOI to facilitate reproducibility.

Circularity Check

1 steps flagged · score 4.0 of 10

The reported Dice superiority is partly constructed by per-subject tuning of Aniso_Thresh on the same subjects whose manual masks define the Dice; the seed-generation and region-growing pipeline itself has no derivation-level circularity.

  1. fitted input called prediction [Methods 2.2 (hyperparameter adjustment), Methods 2.3 and Table 1 (DSC evaluation).]
    "the anisotropy threshold (𝐴𝑛𝑖𝑠𝑜_𝑇ℎ𝑟𝑒𝑠ℎ) was adjusted for each subject with 1.2 × 10-3 as the starting point for both 𝜒𝑝𝑎𝑟𝑎 and |𝜒𝑑𝑖𝑎| maps. In most cases, this starting point value created a high-quality outcome, however, in some cases, the value was increased when deep gray matter regions were not properly excluded."

    The Table 1 Dice values are computed on the same three 3T and three 7T subjects whose Aniso_Thresh was manually adjusted. Raising Aniso_Thresh directly invokes Eq. 7 to delete connected components with low mean |λ2·λ3|, which is precisely the mechanism the paper uses to remove deep gray matter false positives, and those structures are part of the manual ground truth that lowers Dice when included. Thus the reported 'highest Dice' is not an out-of-the-box comparison with the fixed-parameter Frangi baseline; it is a per-subject fitted result on the evaluation data. The central algorithmic machinery (seeds and region growing) retains independent content, so this is partial evaluation circularity rather than a full reduction of the method to its inputs.

full rationale

No self-definitional collapse, renamed known result, or author-imported uniqueness argument was found. The seeds are explicit functions of R2* and chi maps, the region-growing condition is a written geometric criterion, and the refinement is an anisotropy filter; each step is a direct function of the input maps, so the segmentation pipeline is not equivalent to its benchmark by construction. The only defensible circularity is the quantitative claim: Aniso_Thresh was set separately for each subject (Methods 2.2) and the same six subjects produce the Table 1 Dice scores, while the Frangi comparator uses fixed literature parameters; the paper itself admits that 'subject-wise parameter tuning can be challenging' and that fixed parameters exclude some small vessels, which is consistent with this concern. The manual masks also exclude calcifications, meninges, and registration artifacts (Supplementary Fig. 3), another acknowledged evaluation limitation, but this is data selection rather than a circular derivation. Self-citations to chi-sepnet, the chi-separation template, and the atlas are data/tool citations and are not load-bearing circular arguments. Score 4 reflects the partial fitted-input/evaluation circularity; the algorithm's core contribution remains independent.

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

The method introduces no new physical entities. It depends on fixed hyperparameters (gamma1, gamma2, MFAT defaults, seed thresholds) and on domain assumptions about vessel contrast and anisotropy. The per-subject anisotropy threshold is the parameter most likely to affect the reported performance.

free parameters (5)
  • gamma1 = 0.5
    Upper intensity limit in Eq. 1; fixed by the authors, controls inclusion of high-intensity voxels during region growing.
  • gamma2 = -0.5
    Lower intensity limit in Eq. 2; fixed by the authors, controls exclusion of low-intensity voxels.
  • Anisotropy threshold (Aniso_Thresh) = starting 1.2e-3, adjusted per subject up to 0.0108
    Step 3 removes connected components with mean anisotropy below this threshold; tuned per subject on the evaluation data, which may inflate reported Dice scores.
  • Seed vesselness thresholds = mean + 2*std (large vessels), mean + 1*std (small vessels)
    Binarization cutoffs for MFAT vesselness maps in Step 1; data-derived thresholds, not cross-validated.
  • MFAT parameters = sigma=[0.25,1], delta_sigma=0.25, tau_rho=0.02, tau_nu=0.35, delta=0.3
    Default parameters from Alhasson et al., used to compute vesselness; method performance likely depends on these choices.
assumptions (4)
  • domain assumption Vessels appear hyperintense in R2*, chi_para, and |chi_dia| maps, enabling seed generation from these contrasts.
    Step 1 relies on this; Supplementary Fig. 1 shows representative examples but the sensitivity of seed detection is not quantified.
  • domain assumption Tubular vessels have higher anisotropy, measured by |lambda2*lambda3|, than non-vessel structures such as deep gray matter and myelinated fibers.
    Step 3 and Eq. 7 use mean anisotropy per connected component to remove non-vessel structures; this geometric separability is load-bearing.
  • domain assumption Manual segmentation by the authors on chi-separation maps is a valid ground truth for vessels.
    DSC is computed against these labels; inter-rater variability is not reported, and labels exclude calcifications, meninges, and misregistration artifacts.
  • standard math Hessian-based vesselness filters (Frangi, MFAT) correctly encode tubular geometry in 3D MRI data.
    Used in Step 1 and Step 2; this is standard image analysis mathematics and not specific to the paper.

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Cite this review

Pith. "Pith review of Vessel segmentation for X-separation." pith.science (2026). https://pith.science/paper/LJRIUTCR

@misc{pith2026250201023,
  author       = {Pith},
  title        = {Pith review of: Vessel segmentation for X-separation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LJRIUTCR}},
  note         = {Machine review of arXiv:2502.01023}
}
abstract

$\chi$-separation is an advanced quantitative susceptibility mapping (QSM) method that is designed to generate paramagnetic ($\chi_{para}$) and diamagnetic ($|\chi_{dia}|$) susceptibility maps, reflecting the distribution of iron and myelin in the brain. However, vessels have shown artifacts, interfering with the accurate quantification of iron and myelin in applications. To address this challenge, a new vessel segmentation method for $\chi$-separation is developed. The method comprises three steps: 1) Seed generation from $\textit{R}_2^*$ and the product of $\chi_{para}$ and $|\chi_{dia}|$ maps; 2) Region growing, guided by vessel geometry, creating a vessel mask; 3) Refinement of the vessel mask by excluding non-vessel structures. The performance of the method was compared to conventional vessel segmentation methods both qualitatively and quantitatively. To demonstrate the utility of the method, it was tested in two applications: quantitative evaluation of a neural network-based $\chi$-separation reconstruction method ($\chi$-sepnet-$\textit{R}_2^*$) and population-averaged region of interest (ROI) analysis. The proposed method demonstrates superior performance to the conventional vessel segmentation methods, effectively excluding the non-vessel structures, achieving the highest Dice score coefficient. For the applications, applying vessel masks report notable improvements for the quantitative evaluation of $\chi$-sepnet-$\textit{R}_2^*$ and statistically significant differences in population-averaged ROI analysis. These applications suggest excluding vessels when analyzing the $\chi$-separation maps provide more accurate evaluations. The proposed method has the potential to facilitate various applications, offering reliable analysis through the generation of a high-quality vessel mask.

Figures

Figures reproduced from arXiv: 2502.01023 by the authors.

Figure 1
Figure 1. Overview of the proposed pipeline for vessel segmentation. The pipeline has three steps: Step 1 for seed generation, Step 2 for an initial vessel mask created by region growing guided by the characteristics of vessel geometry, and Step 3 for non-vessel structures removal [PITH_FULL_IMAGE:figures/full_fig_p018_1.png] view at source ↗
Figure 3
Figure 3. Results of the vessel segmentation methods applied to |𝜒𝑑𝑖𝑎|. The |𝜒𝑑𝑖𝑎| maps (first column) and the three vessel segmentation masks overlaid on |𝜒𝑑𝑖𝑎| (second column: Frangi filter, third column: GRE-based method, and fourth column: proposed method) are displayed. Three representative slices that include the optic radiation (first row), cortical vessels (second row), and small vessels (third row) reveal that the pr… view at source ↗
Figure 6
Figure 6. Representative ROIs including vessels. Caudate and corpus callosum show the highest vessel portion for 𝜒𝑝𝑎𝑟𝑎 and |𝜒𝑑𝑖𝑎|, respectively. Caudate primarily includes the anterior terminal veins whereas corpus callosum has septal veins (yellow arrows) [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗
Figures from the paper (4 more)
Figure 1
Figure 1. Figure 1: Overview of the proposed pipeline for vessel segmentation. The pipeline has three steps: Step 1 for seed generation, Step 2 for an initial vessel mask created by region growing guided by the characteristics of vessel geometry, and Step 3 for non-vessel structures remov…
Figure 2
Figure 2. Figure 2: Results of the vessel segmentation methods applied to 𝜒𝑝𝑎𝑟𝑎. The 𝜒𝑝𝑎𝑟𝑎 maps (first column) and the three vessel segmentation masks overlaid on 𝜒𝑝𝑎𝑟𝑎 (second column: Frangi filter, third column: GRE-based method, and fourth column: proposed method) are displayed. Three …
Figure 3
Figure 3. Figure 3: Results of the vessel segmentation methods applied to |𝜒𝑑𝑖𝑎|. The |𝜒𝑑𝑖𝑎| maps (first column) and the three vessel segmentation masks overlaid on |𝜒𝑑𝑖𝑎| (second column: Frangi filter, third column: GRE-based method, and fourth column: proposed method) are displayed. Thr…
Figure 6
Figure 6. Figure 6: Representative ROIs including vessels. Caudate and corpus callosum show the highest vessel portion for 𝜒𝑝𝑎𝑟𝑎 and |𝜒𝑑𝑖𝑎|, respectively. Caudate primarily includes the anterior terminal veins whereas corpus callosum has septal veins (yellow arrows) [PITH_FULL_IMAGE:figu…

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Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.