DSF-Net provides a robust framework for improving vessel continuity and boundary delineation in fundus images and produces more accurate and structurally coherent segmentation results, especially for thin and complex vessels.
Abstract
Retinal vessel segmentation is a fundamental task in quantitative fundus image analysis. However, existing methods still face challenges in segmenting thin and complex vessels because local details and global contextual information are often insufficiently integrated.
To address this issue, we propose a Dual Selective Fusion Network (DSF-Net) for retinal vessel segmentation. The proposed network consists of a Dual-Branch Encoder (DB-Encoder), a Pinwheel-based Local Attention (PLA) module, and a Dual Selective Fusion Transformer Block (DSFTB). The DB-Encoder jointly models spatial- and frequency-domain information to capture both fine vessel details and global contextual patterns. The PLA module enhances local perception and boundary sensitivity through asymmetric multidirectional convolutions and Sobel edge priors. The DSFTB integrates Multi-scale Feature Attention (MSFA) and token-selective Global Feature Attention (GFA) to enable adaptive feature fusion and long-range dependency modeling.
Experiments conducted on the DRIVE, STARE, and CHASE_DB1 datasets demonstrate that DSF-Net achieves competitive overall performance compared with existing methods. In particular, the proposed method produces more accurate and structurally coherent segmentation results, especially for thin and complex vessels.
These findings indicate that the combined modeling of local detail, frequency-aware representation, and global contextual dependency is effective for retinal vessel segmentation. DSF-Net provides a robust framework for improving vessel continuity and boundary delineation in fundus images. The source code is available at:
https://github.com/liang050629/DSF-Net
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Accurate segmentation of retinal vessels is a key task in fundus image analysis and is crucial for the early screening and diagnosis of various ophthalmic and systemic diseases. However, existing methods still struggle to simultaneously address scale variation, contextual fusion, and the recovery of directional structures when dealing with retinal vessels, which easily leads to missed detection of thin vessels and structural discontinuities. To tackle these issues, we propose a retinal vessel segmentation network, MAF-Net. First, we design an adaptive multi-scale dilated residual block in the encoder, which performs branch-wise adaptive selection and reweighting over parallel dilated branches to dynamically adjust scale responses, thereby enabling adaptive modeling of vessel features with different calibers. Second, to enhance the network’s ability to understand context, we introduce a windowed hierarchical cross-scale attention fusion module at the bottleneck layer, which enables cross-scale interaction of multi-level features within local windows, thereby efficiently aggregating contextual information while reducing computational complexity. Finally, in the skip connections, we propose a semantically guided tri-axis fusion module, which extracts directional information along the horizontal, vertical, and spatial axes, and uses progressively guided features from lower-resolution layers to generate a spatial confidence map for gated selection, thereby suppressing shallow noise and reducing discontinuities and adhesion. On three public datasets, DRIVE, STARE, and CHASE-DB1, MAF-Net achieves accuracies of 97.14%, 97.99%, and 97.82%, respectively. These results indicate that MAF-Net improves vessel structure recovery while better preserving vascular connectivity and structural consistency.
Yueda Gong, Baoshan Li, Yongxing Du et al.· Biomedical engineering and p...· 0 citations
Accurate retinal vessel segmentation is critical for automated screening and monitoring of ocular and systemic diseases; however, existing deep segmentation models often struggle to preserve thin, low-contrast vessels. We propose a Learnable Multi-Scale Vesselness Attention (LMSA) module, a lightweight and architecture-agnostic component that introduces explicit vessel-aware priors into deep feature learning by adaptively selecting scale-space vesselness responses under a second-order structural prior and refining them through directional coherence modulation. LMSA aggregates multi-scale vesselness responses into a structurally aware representation and transforms them into a spatial attention mask that selectively enhances curvilinear vascular structures while suppressing background noise. Using U-Net as a reference backbone, an LMSA-enhanced network is constructed with an integrated multi-task learning (MTL) design that jointly supervises vessel segmentation, boundary delineation, and centerline extraction, thereby enforcing structural consistency. The generality of the framework is further demonstrated by integrating LMSA and MTL into multiple CNN segmentation architectures. Extensive experiments on three public benchmarks indicate that the proposed U-Net+LMSA+MTL model outperforms state-of-the-art baselines, achieving Dice scores of 85.39%, 85.00%, and 85.47%, with corresponding accuracies of 97.37%, 97.87%, and 97.90% on DRIVE, STARE, and CHASE_DB1, respectively. On a clinically acquired in-house dataset, the model maintains strong performance with a Dice score of 82.77% and an accuracy of 95.88%, supporting its potential for real-world clinical deployment. Collectively, these results demonstrate that the proposed framework effectively embeds vessel prominence and structural priors into deep networks, enabling more reliable, structurally consistent, and clinically meaningful retinal vessel analysis.
Dinoy Johny, V. Chandan, Shubham Loni et al.· Scientific Reports· 0 citations
Retinal vessel analysis in ultra-widefield (UWF) images provides a unique opportunity for large-scale assessment of systemic microvascular health. However, accurate segmentation in true-color UWF images remains challenging due to the large field of view, complex background, and reduced vessel contrast. To address these challenges, we develop ECS-Net, a dedicated deep learning (DL) framework for retinal vessel segmentation in true-color UWF images. Our ECS-Net adopts an enhanced encoder decoder architecture that integrates a dual-domain context enhancement module (DCEM), consisting of an anisotropic spatial focus unit (ASFU) and a feature correlation calibration unit (FCCU), together with atrous spatial pyramid pooling (ASPP) for multi-scale context modeling. The proposed ECS Net achieved a higher Dice coefficient of 0.8349 than other state-of-the-art algorithms (0.7001-0.8136), demonstrating strong generalization under real-world imaging conditions. Building upon accurate vessel extraction, region-specific vascular parameters, including vessel density (VD), fractal dimension (FD), tortuosity (TC), and mean curvature (MC), were quantified separately for central (45°), peripheral (45° 133°), and global (133°) zones. Multivariable logistic regression was used to evaluate associations with metabolic diseases in 4,618 participants. Hypertension showed significant inverse associations with VD and FD across all zones (all P < 0.001). Diabetes exhibited striking regional specificity, characterized by increased TC and MC and decreased FD were confined to the peripheral and global zones (all P< 0.01), with inverse associations for TC and MC detectable only in the global zone (P < 0.05). This study establishes the first DL framework specifically designed for retinal vessel segmentation in true color UWF images and reveals disease-specific regional vascular patterns that would be missed by conventional fundus photography, highlighting the value of UWF imaging for comprehensive systemic disease assessment and providing a biological prior for the development of interpretable and region-aware models. The code will be released on GitHub: https://github.com/yxyXinyue/UWF Retinal-Vasculature-Segmentation-Cardiometabolic.
Xinyue Wang, Xinyue Yang, Yu-Wei Wang et al.· IEEE journal of biomedical a...· 0 citations
Retinal diseases are a leading cause of irreversible vision impairment, making early and accurate diagnosis essential for effective treatment. Optical Coherence Tomography (OCT) serves as a critical imaging modality for this purpose, yet its automated analysis is hindered by inherent speckle noise, varying lesion scales, and subtle inter-class similarities. To address these challenges, we propose a novel framework, RetiWave-Mamba, which integrates spatial-frequency domain learning with state-of-the-art state space models. The framework utilizes Discrete Wavelet Transform (DWT) to decompose OCT images into low- and high-frequency streams, enabling decoupled processing of structural context and fine-grained details. For the low-frequency branch, we design a Multi-scale Contextual Localization Module (MCLM), which synergizes multi-scale dilation with spatial attention to expand the global receptive field and precisely localize lesion regions. For the high-frequency branch, we introduce an Attention-Guided High-Resolution Network (AG-HRNet) equipped with an intelligent gating mechanism to suppress noise propagation during multi-scale interactions. Furthermore, a Frequency-Adaptive Mamba Projector (FAMP) is incorporated to capture long-range dependencies within disjoint high-frequency textural features. Extensive experiments on the OCT-C8 dataset demonstrate that our approach achieves a state-of-the-art (SOTA) classification accuracy of 98.25%, surpassing existing methods. These results highlight the efficacy of RetiWave-Mamba in robustly identifying retinal pathologies under noisy conditions, offering a promising tool for clinical diagnosis.