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REVIEW 4 major objections 4 minor 51 references

A Novel Hybrid Approach for Retinal Vessel Segmentation with Dynamic Long-Range Dependency and Multi-Scale Retinal Edge Fusion Enhancement

T0 review · 4 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read A hybrid CNN-Mamba network with curvature-aware scanning and multi-scale edge fusion reports the best retinal vessel segmentation on three public benchmarks.

desk verdict A competent incremental architecture whose SOTA claim is not backed by the tables as presented; the paper needs protocol transparency and significance testing more than it needs new modules. read the letter →

arxiv 2504.13553 v1 pith:JOVEKS4J submitted 2025-04-18 eess.IV cs.CV

Yihao Ouyang , Xunheng Kuang , Mengjia Xiong , Zhida Wang , Yuanquan Wang This is my paper · ORCID
classification eess.IVcs.CV
keywords retinalvesselsegmentationMambastatespacemodelmulti-scaleedgefusionDynamicSnakeConvolutionfundusimagingmedicalimagelow-contrastvessels
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

Retinal vessel segmentation breaks down in two ways: thin vessels get fragmented and low-contrast boundaries become ambiguous. The paper proposes HREFNet, a hybrid network that addresses both failure modes by pairing a high-resolution-preserving backbone with a curvature-adaptive Mamba block and a multi-scale edge-fusion module. Results on DRIVE, STARE, and CHASE DB1 are reported as the best among the compared CNN, Transformer, and Mamba methods, with the strongest improvements in vessel continuity and edge clarity. The core claim a sympathetic reader should take away is architectural: high-resolution feature preservation, curvature-aware state-space scanning, and multi-scale edge fusion reinforce each other, and ablations show each component contributes to the final gain.

What carries the argument

The load-bearing mechanism is the pair of new modules inserted into a high-resolution-preserving backbone. The DSVSS block couples Dynamic Snake Convolution, whose receptive field grows along a curved path so that it follows vessel geometry, with a Mamba state-space scan extended from four to eight directions and aggregated by learned softmax weights; this is what lets the model trace long, thin, winding vessels without losing continuity. The MREF module runs dilated convolutions at rates 1, 3, and 5, sums their outputs with a residual connection, and applies Squeeze-and-Excitation channel attention so that boundary features are emphasized and background noise suppressed. The argument rests on these modules because the ablations show each one improves the high-resolution baseline, and the combined model scores highest.

What would settle it

Re-run every baseline in Tables 1 to 3 under the exact protocol used for HREFNet: grayscale images, 224-by-224 random crops, AdamW with a learning rate that decays on plateau, 300 epochs, and the same train/test splits. If, under this matched protocol, HREFNet no longer holds the top Dice and clDice on all three datasets, then the central state-of-the-art claim is false.

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Extended reading notes

Core claim

The paper's central claim is that retinal vessel segmentation can be improved by fusing two mechanisms that address different failure modes: a selective-scan state-space block whose receptive field follows vessel curvature, and a multi-scale edge-fusion module that sharpens boundaries. HREFNet places these modules on an HRNet-style high-resolution backbone, replaces the standard blocks with the Dynamic Snake Visual State Space (DSVSS) block, and inserts the Multi-scale Retina Edge Fusion (MREF) module after each stage. The DSVSS block combines Dynamic Snake Convolution with a Mamba state-space scan extended from four to eight directions and aggregates the directional features with learned softmax weights; MREF extracts edge features with dilated convolutions at rates 1, 3, and 5, recalibrates them with channel attention, and fuses them back into the stage output. On this architecture the paper reports the best Dice and clDice scores on DRIVE, STARE, and CHASE DB1 among the compared methods, and the ablation study attributes the improvement to the two modules working together.

Load-bearing premise

The tables assume that every comparison method was measured under the same training and evaluation conditions as HREFNet, but the paper never states whether those baselines were re-trained in-house, which preprocessing was applied, or which splits produced the quoted numbers; if those numbers come from different protocols, the reported margins may reflect protocol differences rather than architectural gains.

Editorial extensions

If this is right

  • On the small training sets typical of retinal datasets, the architecture reports competitive results without pretraining or external data, which matters for clinical settings where annotated images are scarce.
  • Because the ablation shows DSVSS and MREF each add measurable gains and the largest gain comes from their combination, future designs can treat curvature-aware scanning and edge fusion as complementary rather than competing strategies.
  • The eight-directional scan with per-direction softmax weighting is defined as a general mechanism and can be lifted into other Mamba-based segmentation networks to test whether the continuity gain transfers beyond retinal vessels.
  • The reported HD95 improvements imply that the model not only finds more vessel pixels but also places their boundaries more accurately, which is the property most relevant for downstream clinical measurements.

Reading between the lines

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

  • A natural extension is to test whether the same two modules help on other elongated or tubular structures, such as corneal nerves or coronary arteries, where thin, curved, low-contrast targets produce the same fragmentation and blur failure modes.
  • The eight-directional snake scan is architecture-agnostic; dropping it into a Transformer-based segmentation head would tell whether the gain comes from directional scanning per se or from the Mamba state-space backbone.
  • Because the paper reports results from only 20 to 28 training images per dataset, an open question is whether HREFNet's advantage holds on larger or multi-center fundus collections with more domain shift.
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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

4 major / 4 minor

Summary. The paper proposes HREFNet, a hybrid CNN-Mamba architecture for retinal vessel segmentation built on a high-resolution (HRNet-like) backbone. Two components are introduced: the Dynamic Snake Visual State Space (DSVSS) block, which combines dynamic snake convolution with a Mamba-style selective scan extended to eight directions, and the Multi-scale Retina Edge Fusion (MREF) module, which fuses multi-scale dilated features with channel attention. The method is evaluated on DRIVE, STARE, and CHASE DB1 against CNN, Transformer, and Mamba baselines, reporting Dice, clDice, ACC, AUC, and HD95. The paper claims state-of-the-art performance and includes component-level ablations plus a stage-depth study. The code is made publicly available.

Significance. If the performance claims were robust, the paper would make a useful contribution to retinal vessel segmentation by demonstrating that combining curvature-adaptive convolution with state-space long-range modeling and multi-scale edge fusion can improve vessel continuity and boundary quality. The manuscript is honest in reporting several metrics where HREFNet does not win (e.g., ACC on DRIVE, ACC/AUC on CHASE DB1, AUC on STARE), and it ships code, which aids reproducibility. The component ablations on DRIVE are a positive feature. However, the central state-of-the-art claim is not supported by the evidence as presented: the reported margins over the second-best method are in several cases smaller than the reported standard deviations, no significance tests are provided, and the baseline comparison protocol is not described. The contribution is therefore credible as an engineering proposal but not yet established as a benchmark-leading method.

major comments (4)
  1. [§5.1.2, Tables 1–3] A concrete fix is to re-run all baselines under the same preprocessing, training schedule, and evaluation code, and to report paired significance tests (e.g., Wilcoxon signed-rank or bootstrap CIs) for the primary comparisons.
  2. [§5.2.3, Table 6] This point is load-bearing because the final HREFNet configuration is not justified independently of the test-set results.
  3. [§3.1, Eq. (3)] This is a load-bearing technical point for the proposed architecture and should be corrected in a revision.
  4. [§3.2, Eqs. (7)–(8)] If the weights are indeed static, the authors should revise the terminology and the motivation accordingly; if the weights are input-dependent, the mechanism must be specified precisely.
minor comments (4)
  1. [§4.2.2, Eq. (14)] The clDice definitions reuse the symbols SP and VL for both the binary masks and their skeletons, which makes Eq. (14) confusing: Tprec is written as |SP ∩ VL| / |SP| without clearly indicating that one of these sets is a skeleton. Please use distinct symbols, e.g., S_P and S_L for skeletons, and correct the definitions accordingly.
  2. [References, [42]] The citation for CHASE DB1 is incorrect: reference [42] is a paper on coronary angiography segmentation, not the CHASE dataset. Please cite the original CHASE DB1 source (Owen et al.) or the standard dataset reference.
  3. [§2.1] The text contains a typo, 'Rencently', and the term 'Clustering Dice' is nonstandard; the topology-aware metric is conventionally called clDice, not 'Clustering Dice'. Please correct the spelling and terminology.
  4. [§5.1.2 and §4.3] The evaluation procedure is not fully specified: the paper does not state whether inference is performed on full-resolution images or on overlapping patches, whether test-time augmentation is used, or how the final segmentation is thresholded (fixed 0.5, Otsu, etc.). Description of these details would improve reproducibility of Tables 1–3.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: HREFNet is an empirical architecture paper whose benchmark claims rest on experiments, not on a derivation that reduces to its own inputs.

full rationale

HREFNet is an empirical deep-learning paper: the central claim is that the proposed architecture achieves state-of-the-art retinal vessel segmentation on DRIVE, STARE, and CHASE DB1, supported by comparative tables and ablation studies. The named innovations are combinations and extensions of externally cited building blocks — HRNet as a high-resolution backbone, Dynamic Snake Convolution, Mamba/SS2D scanning, and SE attention — and none of the equations defines a target quantity in terms of the quantity it is supposed to predict. The DSVSS and MREF modules are described operationally (Eqs. 1–12) and their contributions are measured in ablations rather than derived analytically; the reported Dice/clDice/ACC/AUC/HD95 numbers are experimental outcomes, not identities implied by the inputs. No load-bearing self-citation chain was found: the cited prior works are external sources for standard components, and the paper does not invoke a uniqueness theorem or a prior result from the same authors to forbid alternatives. The weaknesses noted by a skeptical reader — possible lack of protocol control for baseline numbers and selection of HREFNet-large using DRIVE test-set results in Table 6 — are experimental-design and statistical-validity concerns, not circularity in the sense of a prediction being equivalent to its inputs by construction. Therefore the appropriate circularity score is 0.

Assumptions & free parameters 4 free parameters · 4 assumptions · 2 invented entities

The central claim rests on the proposed DSVSS and MREF modules, whose components are drawn from prior work (DSConv, SS2D, SE) with hand-chosen architectural choices (dilation rates, stage count, scan directions). The paper provides ablations on DRIVE for each component, but all free parameters are tuned or selected with the test set in view, and the modules themselves have no independent empirical support.

free parameters (4)
  • MREF dilation rates = (1, 3, 5)
    Hand-chosen in Eq. (9) to capture multi-scale edge context; no sensitivity analysis or theoretical justification.
  • DSConv curvature modulation factor e = Not reported
    Appears in Eq. (6) as a curvature modulation hyperparameter; the paper does not specify its value or tuning procedure.
  • Network stage depth = 4 (HREFNet-large)
    Selected from Table 6 based on best Dice on the DRIVE test set; the middle variant performs worse than tiny, indicating the choice is data-dependent.
  • Number of scanning directions = 8
    Extended from SS2D's 4 directions by hand; no comparison with 4, 6, or other scan configurations is provided.
assumptions (4)
  • domain assumption Vessels are curvilinear structures, so direction-adaptive convolutions and directional scans are beneficial inductive biases.
    Motivates DSVSS in Section 3.2; the ablation shows only small gains, so the benefit is not strongly evidenced.
  • domain assumption Multi-scale edge fusion with dilated convolutions and SE attention improves boundary accuracy without semantic drift.
    Justified only by the DRIVE ablation in Table 5; the mechanism is not analyzed.
  • domain assumption Converting RGB fundus images to grayscale preserves all vessel-relevant discriminative information.
    Stated in Section 4.3 without comparison to color input.
  • ad hoc to paper The outputs Q1 through Q4 in Eq. (3) are implicitly resampled to the same resolution before element-wise multiplication.
    Required to make Eq. (3) well-defined; the paper never states this resampling.
invented entities (2)
  • Dynamic Snake Visual State Space (DSVSS) Block
    purpose: Combines DSConv and Mamba S6 with an 8-direction snake selective scan to model vessel curvature and long-range dependencies.
    Effectiveness rests only on the paper's own ablation (Table 5); no external validation or code verification.
  • Multi-scale Retina Edge Fusion (MREF) Module
    purpose: Aggregates multi-scale dilated convolution features with SE attention to enhance vessel edges.
    Only evidence is the paper's ablation; not verified by external benchmarks.

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Cite this review

Pith. "Pith review of A Novel Hybrid Approach for Retinal Vessel Segmentation with Dynamic Long-Range Dependency and Multi-Scale Retinal Edge Fusion Enhancement." pith.science (2026). https://pith.science/paper/JOVEKS4J

@misc{pith2026250413553,
  author       = {Pith},
  title        = {Pith review of: A Novel Hybrid Approach for Retinal Vessel Segmentation with Dynamic Long-Range Dependency and Multi-Scale Retinal Edge Fusion Enhancement},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JOVEKS4J}},
  note         = {Machine review of arXiv:2504.13553}
}
read the original abstract

Accurate retinal vessel segmentation provides essential structural information for ophthalmic image analysis. However, existing methods struggle with challenges such as multi-scale vessel variability, complex curvatures, and ambiguous boundaries. While Convolutional Neural Networks (CNNs), Transformer-based models and Mamba-based architectures have advanced the field, they often suffer from vascular discontinuities or edge feature ambiguity. To address these limitations, we propose a novel hybrid framework that synergistically integrates CNNs and Mamba for high-precision retinal vessel segmentation. Our approach introduces three key innovations: 1) The proposed High-Resolution Edge Fuse Network is a high-resolution preserving hybrid segmentation framework that combines a multi-scale backbone with the Multi-scale Retina Edge Fusion (MREF) module to enhance edge features, ensuring accurate and robust vessel segmentation. 2) The Dynamic Snake Visual State Space block combines Dynamic Snake Convolution with Mamba to adaptively capture vessel curvature details and long-range dependencies. An improved eight-directional 2D Snake-Selective Scan mechanism and a dynamic weighting strategy enhance the perception of complex vascular topologies. 3) The MREF module enhances boundary precision through multi-scale edge feature aggregation, suppressing noise while emphasizing critical vessel structures across scales. Experiments on three public datasets demonstrate that our method achieves state-of-the-art performance, particularly in maintaining vascular continuity and effectively segmenting vessels in low-contrast regions. This work provides a robust method for clinical applications requiring accurate retinal vessel analysis. The code is available at https://github.com/frank-oy/HREFNet.

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.