Title: Towards Generalizable Retina Vessel Segmentation with Deformable Graph Priors
Venue: NeurIPS 2025
Accepted: yes
Keywords: probabilistic modeling
Abstract: Retinal vessel segmentation is critical for medical diagnosis, yet existing models often struggle to generalize across domains due to appearance variability, limited annotations, and complex vascular morphology. We propose GraphSeg, a variational Bayesian framework that integrates anatomical graph priors with structure-aware image decomposition to enhance cross-domain segmentation. GraphSeg factorizes retinal images into structure-preserved and structure-degraded components, enabling domain-invariant representation. A deformable graph prior, derived from a statistical retinal atlas, is incorporated via a differentiable alignment and guided by an unsupervised energy function. Experiments on three public benchmarks (CHASE, DRIVE, HRF) show that GraphSeg consistently outperforms existing methods under domain shifts. These results highlight the importance of jointly modeling anatomical topology and image structure for robust generalizable vessel segmentation.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes GraphSeg, a novel variational Bayesian framework for retinal vessel segmentation that addresses the challenge of cross-domain generalization. GraphSeg builds upon Bayesian approaches such as BayeSeg and Indeed, and additionally integrates deformable graph priors derived from a statistical retinal atlas, enabling domain-invariant representations. A probabilistic graphical model (PGM) is introduced to jointly model images, vascular shapes, and graph priors, with a differentiable alignment mechanism guided by an unsupervised energy function. The framework leverages implicit neural representations and Gaussian splatting to align graph priors with image structures. Experiments on three public datasets—CHASE, DRIVE, and HRF—demonstrate GraphSeg’s superior performance, particularly in cross-domain settings, outperforming methods like the supervised trained U-Net, BayeSeg, and FSG-Net.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. Well-Motivated Research Problem. Retinal vessel segmentation is critical for diagnosing retinal diseases, but it faces challenges due to limited annotated data and significant domain variability across multi-center datasets. The paper effectively motivates the use of graph priors, specifically a statistical retinal atlas, to enhance cross-domain generalization. \n2. Innovative Framework. The integration of a statistical retinal atlas as a deformable graph prior is a novel contribution. The use of retinal atlas derived from the CHASE dataset, combined with a variational Bayesian framework, introduces a fresh perspective to vessel segmentation. The ablation study in Table 3 convincingly demonstrates the effectiveness of the graph prior in improving generalization, with notable improvements in Soft Dice when trained on CHASE and tested on DRIVE.\n3 . Comprehensive Methodology. GraphSeg combines multiple advanced techniques, including Bayesian modeling, graph construction with shape PCA, graph neural networks, and Gaussian splatting for implicit neural representation. The method's design is thorough, with a clear probabilistic graphical model (Fig. 1) and a detailed network architecture (Fig. 2). The use of an unsupervised energy function to guide structural alignment is particularly noteworthy, enabling the model to capture long-range vascular dependencies effectively.\n\nWeaknesses\n1. Insufficient Experimental Validation. \na) Preprocessing Dependency: the paper acknowledges the dependency on pre-processing for skeleton extraction and graph construction, which involve heuristic rules for junction merging and branch pruning. However, the extent of manual intervention in these steps is not clearly quantified, which could potentially lead to unfair comparisons with other methods. The authors should clarify the degree of human involvement and the robustness of these heuristics.\nb) Limited Cross-Dataset Evaluation: while GraphSeg emphasizes strong cross-domain generalization, the evaluation is limited to three datasets (CHASE, DRIVE, HRF). To substantiate the claim of robust generalization, additional datasets such as STARE, DRHAGIS, LES-AV, and RETA should be included in Table 2 to cover a broader range of imaging conditions and vascular morphologies. \nc) Limited Comparison Methods: The cross-dataset evaluation in Table 2 includes only U-Net and BayeSeg as comparison methods. More comparison methods are needed for Cross-dataset evaluation.\n2. Unclear Presentation. \na) Vascular Structure Matching: The explanation of the vascular structure matching in Section 3.2 is not sufficiently clear. Specifically, the variable d in Equation (1) lacks a clear definition of its meaning and value range. \nb) Comparison with BayeSeg: A more detailed comparison highlighting the technical distinctions and advantages would strengthen the contribution’s novelty. \nc) Lack of Code Availability: Providing an anonymized code repository, including preprocessing steps, at submission would enhance trust in the results.\n\nQUESTIONS:\n1. Is GraphSeg the first method to use a retinal atlas or Bayesian framework for retinal vessel segmentation, or are there prior works that have explored similar anatomical priors? If so, how does GraphSeg differ from these approaches?\n2. Given that the retinal atlas is derived from the CHASE dataset, how consistent is its distribution with those of other datasets like DRIVE and HRF, which may differ in imaging perspectives or conditions? Does the constructed retinal atlas generalize well across diverse datasets?\n3. How does GraphSeg compare to FSG-Net, U-Net, and BayeSeg in terms of model parameters, computational cost (e.g., FLOPs), and training/testing speed? A quantitative comparison would help assess the practical deployability of GraphSeg.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose GraphSeg, a variation of Bayesian framework.\nThey introduce a deformable graph prior module in training this network and design a statistical model to align the vasculature and deformable graph.\nTheir deisgn demonstrates superior performance comparing to SOTA while remaining good generalizability.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. Clarity: The writing is clear and easy to follow.\n2. Significance: \n - Strong segmentation performance on classic benchmarks.\n - Generalization is a practical challenge in medical imaging. The authors explore the potential of Bayesian frameworks to address this issue.\n3. Originality: \nThe use of retinal priors, such as landmark detection, reflects a solid integration with real-world clinical scenarios. I personally appreciate this practical approach.\n\nWeaknesses:\n1. Quality:  \n - The experiments may not fully support the claimed superiority of the method. There is a lack of repeated trials to assess consistency and robustness.\n - Scale of Data: The dataset used is relatively small, which may limit the generalizability of the findings.\n - The cross-dataset generalization experiments are limited and not comprehensive enough to confirm the method’s robustness.\n\n2. Originality: The limitation in experiments makes it difficult to judge the reliability and practical value of the method. Since the paper use many exisiting ideas without novel changes such as landmark detection, Bayesian modeling, or image decomposition, the paper should at least provide strong experimental evidence (not just in discussion) to show the advantage of combining these ideas (why not the others) or highlight some design principles of the framework. These may help the paper demonstarate the uniqueness of their pipeline.\n\nQUESTIONS:\n1. Graph Atlas is mentioned four times in the paper. Could you please clarify how the retinal vascular graph atlas is initially obtained?\n2. In the cross-dataset evaluation, it is unclear whether pre-processing and decomposition were applied consistently across all compared methods, or only to the proposed approach. Could you provide a cross-dataset comparison regarding pre-processing? Pre-processing can significantly affect the assessment of generalization ability, and its impact should be clearly demonstrated. \n3. The datasets used in the paper are relatively small and dated (a total of 28+40+45=113 images from 2004, 2013, and 2013). To better validate the performance and robustness of the proposed model, it would be important to include more recent and larger-scale datasets.\n4. In Table 3, sensitivity increases significantly with the addition of more components. Could you explain the reason behind this?\n\nI will raise Originality if 4 and 3 are accomodated; will raise Quality if 1,2,3 are solved.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

comments: SUMMARY:
The paper introduces a novel method for segmenting vascular structures in color fundus photographs.  The main novelty of the method is to use a deformable graph prior for the vascular network within a Bayesian segmentation framework.  Results show modest improvements against a reasonable set of baselines on several data sets from different sources.  However, the strongest result is in showing greater improvements against baselines in cross-data-set tasks, i.e. training on one and testing on the other.  This suggests greater robustness and generalizability of the proposed approach.

STRENGTHS AND WEAKNESSES:
Strengths

This is an important problem with clear clinical utility.  

The methods appear sound and well thought out.

The experiments and clear and the results nicely demonstrate potential benefit.

Weaknesses

Does not distinguish veins and arteries unlike other recent work of Zhou et al (in particular https://www.sciencedirect.com/science/article/abs/pii/S1361841524000239 but also https://tvst.arvojournals.org/article.aspx?articleid=2783477), which should be considered in the related work and discussion.  

Does not consider the effect of pathology in the experimental evaluation.

QUESTIONS:
Does the presence of pathology affect performance?  An algorithm that works on normal cases (the majority) but fails in the presence of pathology has little clinical utility.

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-01

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes GraphSeg, a method that addresses cross-domain generalization in retinal vessel segmentation by combining deformable graph priors with structure-aware image decomposition. Experiments on three public datasets (CHASE, DRIVE, HRF) demonstrate consistent improvements over existing methods under domain shift, validating the importance of jointly modeling anatomical topology and image structure.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The integration of deformable graph priors into a Bayesian segmentation framework represents a significant methodological advancement beyond conventional shape-based priors, specifically addressing the complex vascular morphology challenges.\n2. The method demonstrates superior performance on unseen scenarios across multiple datasets, addressing a critical limitation in medical image analysis where domain adaptation is essential for clinical deployment.\n\nWeakness:\n1. The method requires preprocessing steps to extract vessel skeletons from segmentation masks and construct graph representations. This introduces potential variability due to heuristic morphological operations (e.g., junction merging, branch pruning) and limits the end-to-end learning capability of the framework.\n2. The combination of variational Bayesian inference with deformable graph alignment may introduce significant computational overhead compared to standard CNN-based approaches. The paper lacks a discussion of runtime complexity and scalability to higher image resolutions.\n\nQUESTIONS:\n1. The paper lacks a comprehensive analysis of the computational overhead introduced by the variational Bayesian framework and deformable graph alignment.\n2. How sensitive is the method to the quality and representativeness of the statistical retinal atlas used for graph prior construction?\n3. The paper appears to lack a comprehensive comparison with recent end-to-end deep learning approaches for vessel segmentation (e.g., U-Net variants, Transformer-based methods, recent CNN architectures).\n\nLIMITATIONS:\nThe authors acknowledge the dependency on preprocessing steps for skeleton extraction and graph construction, which represents a major technical limitation. However, the paper also lacks computational complexity discussion, comparison with state-of-the-art end-to-end methods, and failure case analysis specific to the proposed approach.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents a learning-based Bayesian retinal vessel segmentation method to address classical Bayesian image segmentation models focusing on compact shapes. By proposing a deformable retinal graph prior, the proposed method can match heterogeneous vascular structures and achieve spatial alignment between the vessel branches and deformable graph priors. The Proposed method has been demonstrated on three retinal vessel datasets. While the results of the comparison study for each dataset show comparable performance between the proposed method and the baseline models, the generalization test results of the proposed method show superior performance over the baseline models.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n- The proposed method handles one of the challenging problems of vessel segmentation in matching heterogeneous vessel structures.\n- The proposed deformable graph prior effectively guides the image decomposition and the vessel segmentation through a variational Bayesian framework.\n- The proposed method shows high generalizability performance when evaluating the model on different datasets from the training dataset.\n\n### Weaknesses\n- While there are many learning-based vessel segmentation methods that have been presented [1, 2, 3] recently and showed high generalization performance, the paper compares the proposed method to only three methods. \n- The proposed method requires a higher computational cost to perform vascular structure matching in the inference stage, compared to the methods that only use neural networks.\n- The performance improvement of the proposed method in the generalizability study is quite marginal.\n\n[1] Li, Yang, et al. \"Diffusion probabilistic learning with gate-fusion transformer and edge-frequency attention for retinal vessel segmentation.\" IEEE Transactions on Instrumentation and Measurement (2024).\n\n[2] Kim, Boah, et al. \"C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation.\" Medical Image Analysis 91 (2024): 103022.\n\n[3] Liu, Jianhua, et al. \"HRD-Net: High resolution segmentation network with adaptive learning ability of retinal vessel features.\" Computers in Biology and Medicine 173 (2024): 108295.\n\nQUESTIONS:\nPlease refer to the above weaknesses.\n\n- It is uncertain whether the proposed method shows state-of-the-art performance when comparing the model to recent vessel segmentation methods.\n- Please provide the computational cost comparison.\n- Please perform a statistical test to see whether the performance of the proposed method is statistically different from the baseline models.\n\nLIMITATIONS:\nyes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}