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REVIEW 3 major objections 6 minor 82 references

Optimized Vessel Segmentation: A Structure-Agnostic Approach with Small Vessel Enhancement and Morphological Correction

T0 review · 3 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A single model segments vessels across four imaging modalities, with 34.6% better connectivity than SAM-based rivals.

desk verdict A useful SAM-based vessel segmenter whose internal comparison is solid, but the 'surpass 17 expert models' claim relies on scores imported from heterogeneous prior papers and is not established. read the letter →

arxiv 2411.15251 v1 pith:OW32T4TU submitted 2024-11-22 eess.IV cs.AIcs.CV

Dongning Song , Weijian Huang , Jiarun Liu , Md Jahidul Islam , Hao Yang , Shanshan Wang This is my paper · ORCID
classification eess.IVcs.AIcs.CV
keywords vesselsegmentationstructure-agnosticSegmentAnythingModelmicroenhancementmorphologicalcorrectiontopologypreservationClDicemulti-modality
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 show that one vessel-segmentation model can work across four imaging modalities instead of needing a separate specialist per anatomy. It argues that the key obstacles are small, low-contrast vessels and broken connectivity, and that both can be addressed by augmenting the Segment Anything Model with a micro-vessel branch and a post-processing network that repairs disconnected segments. If the claim holds, a single model could serve coronary, retinal, OCTA, and pelvic vessel analysis while producing fewer broken vessels.

What carries the argument

The central mechanism is a hybrid encoder built on SAM: the frozen ViT image encoder acts as the macro-vessel extraction branch, while a ConvNeXt-based micro-vessel enhancement branch with CBAM attention and a feature pyramid captures fine vascular details; cross-attention fuses the two branches. The mask decoder then merges low-level ConvNeXt features into SAM's decoder features to restore boundary detail. A separate U-Net post-processing network, trained with MSE and ClDice losses, takes the initial mask and repairs broken vessel segments while preserving natural discontinuities.

What would settle it

Run all 17 expert models on the exact training, validation, and test splits used for OVS-Net and recompute Dice, IoU, ClDice, and $\beta_0$; the claim of surpassing expert models fails if a majority of the re-run experts match or exceed OVS-Net's scores.

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Extended reading notes

Core claim

The paper claims that OVS-Net, a structure-agnostic adaptation of SAM, outperforms both fine-tuned SAM variants and 17 task-specific expert models on multi-modality vessel segmentation, while improving vascular connectivity by 34.6% over the best SAM-based baseline. The evidence is a compiled benchmark of 17 datasets spanning X-ray coronary artery, fundus retina, OCTA retina, and X-ray pelvic iliac artery images, evaluated with Dice, IoU, ClDice, and the normalized Betti number $\beta_0$. The authors present OVS-Net's gains as coming from its hybrid encoder and a morphology-repair post-processor, and report that it also holds up on two held-out external datasets.

Load-bearing premise

The paper assumes that Dice and IoU numbers quoted from earlier expert-model papers are directly comparable to its own scores, even though those models were trained on different data splits, preprocessing, and sometimes datasets.

Editorial extensions

If this is right

  • A single OVS-Net could replace multiple anatomy-specific vessel segmenters for clinical deployment, cutting the need for retraining per modality.
  • Connectivity gains of the reported size mean the output is more likely to support hemodynamic and topological analyses that overlap-based metrics alone do not capture.
  • The design suggests that freezing a large ViT encoder and training only adapters plus a small CNN branch can yield a generalizable medical segmentation model.
  • The morphology-repair post-processor could be attached to other base segmenters, not just OVS-Net, to improve their vessel connectivity.
  • Reporting $\beta_0$ alongside Dice could become a standard practice for evaluating tubular-structure segmentation quality.

Reading between the lines

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

  • The same post-processing idea may transfer to other thin, branching structures such as neurons, roads, or river networks, where fragmentation is the dominant failure mode.
  • If the comparability of quoted expert-model scores is set aside, the most robust evidence in the paper is the internal SAM-based comparison, where all baselines were retrained under the same protocol.
  • A natural next test is whether the model still generalizes to 3D vessel data or to modalities absent from the 17 datasets, such as MRI angiography, since the current claim is limited to 2D images.
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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 proposes OVS-Net, a SAM-based vessel segmentation framework with a frozen ViT encoder plus feature/spatial adapters for macro-vessel extraction, a ConvNeXt-based micro-vessel enhancement branch, mask-decoder feature fusion, and a U-Net post-processing network trained with MSE and clDice losses to repair disconnected vessels. The authors compile 17 multi-modality datasets (coronary X-ray, fundus, OCTA, pelvic iliac X-ray), train on 15, and evaluate on two held-out fundus datasets, comparing against six SAM-based methods retrained under the same protocol and against 17 expert models whose scores are imported from prior publications. They report the highest average Dice, IoU, and clDice with the lowest β0 among SAM-based methods, a 34.6% connectivity improvement, and claim superior accuracy and generalization over expert models. Ablations show incremental gains from the adapter, micro-vessel enhancement, feature connection, and post-processing modules.

Significance. If the controlled SAM-based comparison is taken at face value, the paper makes a useful contribution: a single structure-agnostic model that improves both overlap metrics and connectivity on multi-modality vessel segmentation, supported by a large compiled benchmark and ablations that attribute gains to specific modules. The planned release of code and data is valuable for reproducibility. However, the headline claim of surpassing 17 expert models rests on literature-imported scores with heterogeneous protocols and is not established. The paper's core technical contribution and its evaluation against retrained SAM baselines are sound, but the expert-model comparison needs to be either re-run under a common protocol or explicitly reframed as context rather than head-to-head superiority.

major comments (3)
  1. [Section IV.C, Tables III–VI] The central claim that OVS-Net surpasses 17 expert models is supported only by scores imported from references [55]–[59] and [46] without re-running those models under the same training and evaluation protocol. Table I shows that OVS-Net uses its own random splits and 1024×1024 resizing, whereas the cited expert scores come from heterogeneous pipelines with different splits, preprocessing, and metric conventions; the paper also states that only [57] reports standard deviations and that the best published Dice was selected for comparison. Because several reported margins are small (e.g., DRIVE Dice 81.93 vs 81.41, IOSTAR Dice 78.82 vs 77.16, HRF Dice 77.14 vs 76.74), protocol differences of this size could change the ranking. The "surpassing 17 expert models" claim is therefore not established. I recommend either re-running expert baselines under the OVS-Net protocol or restricting the superiority claim to the controlled SAM-based comparison and presenting the expert-model numbers as contextual literature benchmarks rather than as head-to-head evidence.
  2. [Section III.E] The morphological correction post-processing network is a key module, and the ablation in Table VIII attributes the β0 reduction from 0.48 to 0.34 to it. However, the text does not specify how the "fragmented" training masks are generated: what corruption is applied, to what fraction of masks, and how the target mask is defined. The description "we mask the fragmented areas of the vascular masks" leaves the data-generation procedure underspecified. Without a precise algorithm or pseudocode, the post-processing network cannot be reproduced, and the reported connectivity gain cannot be independently verified. Please provide the full procedure, including any parameters for simulating disconnections.
  3. [Section IV.B/C and Conclusion] The abstract and conclusion claim "robust generalization across diverse imaging modalities," but the external validation (Tables II and VI) uses only two fundus datasets, HRF and IOSTAR, which are the same modality as several training datasets. This tests cross-dataset generalization within a seen modality, not generalization to an unseen modality. The internal test sets do cover four modalities, but the external evaluation does not support the cross-modality generalization claim as stated. Please either add an external dataset from a modality not present in training or temper the wording to "generalization to unseen datasets of a seen modality."
minor comments (6)
  1. [Section IV.A, β0 definition] The formula for β0 is typeset incorrectly: "β0 = 1/N NX⟩=1 |CC (Xi) − CC (Yi)|" should use a summation symbol over i, and the text should clarify that CC(Xi) and CC(Yi) denote the numbers of connected components in patch i.
  2. [Table II, Pelvic Iliac row] For OVS-Net on the Pelvic Iliac artery dataset, β0 is reported as 0.05±0.61; a standard deviation larger than the mean suggests a typo or a unit inconsistency, and all other β0 standard deviations are much smaller relative to their means.
  3. [Section IV.C, Table VI] The text states OVS-Net achieves an average Dice of 79.19 on IOSTAR, but Table VI lists 78.82; one of these is incorrect and should be corrected.
  4. [Section IV.C, Table IV] The text says "On all three fundus datasets, our method consistently achieves the highest average Dice and IoU scores," but Table IV shows CS-Net has a higher IoU on DRIVE (70.17 vs 69.43); this internal contradiction should be fixed.
  5. [References] Reference [56] duplicates reference [20] (both cite the Full-Resolution Network paper by Liu et al.); please remove the duplicate and renumber.
  6. [Figure 5 caption] The caption says "we use the standard deviation as the error bar standard," but the expert-model scores in Tables III–VI mostly have no standard deviations, so it is unclear what error bars are shown; clarify the source of the error bars or remove the claim.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the claims are empirical measurements against held-out test sets, and the literature-imported expert scores are a comparability risk rather than a self-referential derivation.

full rationale

The paper's central claims are empirical benchmark results: OVS-Net is trained on a compiled 17-dataset corpus and evaluated with Dice, IoU, ClDice, and a normalized Betti number on held-out test splits. No equation defines the model's predictions in terms of these metrics, and the architecture is not fitted to the reported outcomes by construction. The post-processing network is trained with MSE and ClDice loss, but the reported connectivity improvement is measured on test data with ClDice and beta0; optimizing a loss function during training does not make the evaluation circular. The only apparent self-citation, reference [2] (S. Wang et al.), supports a background statement about manual annotation being time-consuming and is not load-bearing. The expert-model comparison imports scores from prior papers and selects the best published Dice figure for each dataset; this is a protocol-comparability and selection-bias concern, not a circularity, because the imported numbers are not derived from OVS-Net's own fitted parameters or equations. The ablation study incrementally adds components and reports Dice and beta0 changes, which is a standard empirical attribution rather than a self-justifying derivation. No load-bearing step reduces to its own input, so no circular step is identified.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical or mathematical entities; all modules are composed of existing network components (ViT, ConvNext, CBAM, FPN, U-Net). The novelty is architectural assembly, not a new postulated entity. The major unverified inputs are the comparability of literature expert scores and the transferability of SAM features.

free parameters (6)
  • learning_rate = 0.001
    Chosen for both training stages in Section IV.A; affects convergence and final metrics.
  • training_epochs_primary = 20
    Set in Section IV.A for the segmentation network; not justified by convergence analysis.
  • training_epochs_postproc = 5
    Set in Section IV.A for the U-Net post-processor.
  • input_resolution = 1024x1024
    All images resized to this resolution in Section IV.1; high-resolution datasets are downsampled and small ones upsampled, altering vessel scales.
  • beta0_patch_size = 64x64
    The connectivity metric in Section IV.A splits masks into 64x64 patches; the patch size directly changes beta-0 values and the reported '34.6% connectivity improvement'.
  • convnext_block_config = 3,3,9,3
    Micro module stage depths chosen in Section III.C with feature dimensions 96, 192, 384, 768.
assumptions (5)
  • domain assumption SAM ViT-B features pre-trained with MAE transfer to vascular images.
    The macro branch freezes ViT and trains only adapters (Section III-B); if the pretrained representation is not useful for vessels, the module cannot succeed.
  • domain assumption ConvNext + CBAM features improve small-vessel detection relative to ViT alone.
    The micro vessel enhancement module is justified only by the ablation, not by an explicit mechanism.
  • domain assumption A U-Net trained with MSE and ClDice on fragmented masks can repair disconnected vessels while preserving true gaps.
    Post-processing network design in Section III-E assumes the repair task is learnable from mask pairs; the paper provides no formal or theoretical guarantee.
  • ad hoc to paper Scores reported in cited expert-model papers are directly comparable to OVS-Net scores despite differing training protocols and splits.
    Section IV-C imports expert scores from [55]-[59] and [46] without retraining on the same data, so comparability is assumed.
  • domain assumption Resizing all images to 1024x1024 preserves fine-vessel information across modalities.
    Section IV.1 applies a single resolution to all datasets; for high-resolution fundus images this is a large downsampling.

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Cite this review

Pith. "Pith review of Optimized Vessel Segmentation: A Structure-Agnostic Approach with Small Vessel Enhancement and Morphological Correction." pith.science (2026). https://pith.science/paper/OW32T4TU

@misc{pith2026241115251,
  author       = {Pith},
  title        = {Pith review of: Optimized Vessel Segmentation: A Structure-Agnostic Approach with Small Vessel Enhancement and Morphological Correction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OW32T4TU}},
  note         = {Machine review of arXiv:2411.15251}
}
read the original abstract

Accurate segmentation of blood vessels is essential for various clinical assessments and postoperative analyses. However, the inherent challenges of vascular imaging, such as sparsity, fine granularity, low contrast, data distribution variability, and the critical need for preserving topological structure, making generalized vessel segmentation particularly complex. While specialized segmentation methods have been developed for specific anatomical regions, their over-reliance on tailored models hinders broader applicability and generalization. General-purpose segmentation models introduced in medical imaging often fail to address critical vascular characteristics, including the connectivity of segmentation results. To overcome these limitations, we propose an optimized vessel segmentation framework: a structure-agnostic approach incorporating small vessel enhancement and morphological correction for multi-modality vessel segmentation. To train and validate this framework, we compiled a comprehensive multi-modality dataset spanning 17 datasets and benchmarked our model against six SAM-based methods and 17 expert models. The results demonstrate that our approach achieves superior segmentation accuracy, generalization, and a 34.6% improvement in connectivity, underscoring its clinical potential. An ablation study further validates the effectiveness of the proposed improvements. We will release the code and dataset at github following the publication of this work.

Figures

Figures reproduced from arXiv: 2411.15251 by the authors.

Figure 1
Figure 1. The distribution of vascular imaging data shows significant variation [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Framework diagram of OVS-Net, which comprises five main modules: the ViT-based macro vessel extraction module, the CNN-based micro vessel [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Comparison with other SAM-based methods. From top to bottom, the images are from XCAD, ORVS, DrSAM and OCTA500-3M datasets. Due [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Radar chart of scores on complex vascular datasets compared with [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Bar chart comparing the scores of OVS-Net and the expert model. Here we use the standard deviation as the error bar standard. For expert model [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Feature map visualization. We selected the four channels with the highest average activation values for visualization. The feature maps after incorporating [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: Post-processing effect visualization. The blue boxes indicate preserved [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]

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Pith tools

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