mlsanityData to 14 Oct 2026

Polygon-mamba: Retinal vessel segmentation using polygon scanning mamba and space-frequency collaborative attention

arXiv 2605.10581 ↗First posted 11 May 2026cs.CVcited by 0

On the map: State-space sequence models (S4, Mamba) → Mamba for medical image analysis

Abstract (arXiv)

Retinal vessel segmentation is crucial for diagnosis and assessment of ocular diseases. Notably, segmentation of small retinal vessels has been consistently recognized as a challenging and complex task. To tackle this challenge, we design a hybrid CNN-Mamba fusion network that integrates polygon scanning mamba and space-frequency collaborative attention mechanism for the detection of small vessels. Considering that the traditional mamba architecture with horizontal-vertical scanning may compromise the topological integrity of target structures and result in local discontinuities in small retinal vessels, we present a polygon scanning visual state space model (PS-VSS) to identify small vessel structural features by multi-layer reverse scanning way. Which effectively preserves pixels connectivity, thereby substantially mitigating the loss of information pertaining to small vessels. Furthermore, as we all known that the spatial domain prioritizes positional and structural information, while the frequency domain emphasizes global perception and local detail components, a space-frequency collaborative attention mechanism (SFCAM) is introduced within the skip connection to extract efficient features from the spatial and frequency domains. This strategy empowers the model to dynamically enhance the key features while effectively suppressing clutters. To assess the efficacy of our model, it was tested on three publicly available datasets: DRIVE, STARE, and CHASE_DB1. Compared to manual annotations, our model demonstrated F1 scores of 0.8283, 0.8282, and 0.8251, Area Under Curve (AUC) values of 0.9806, 0.9840, and 0.9866, and Sensitivity (SE) values of of 0.8268, 0.8314, and 0.8484 across three datasets, respectively. The effectiveness of our model was validated through both visual inspection and quantitative analysis.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Mamba for medical image analysis, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 22 of its 53 references that are in the map. First posted on arXiv 11 May 2026.

Builds on

22 of 53 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation24 Apr
  2. 2024S3-Mamba: Small-Size-Sensitive Mamba for Lesion Segmentation19 Dec
  3. 2024Serp-Mamba: Advancing High-Resolution Retinal Vessel Segmentation with Selective State-Space Model6 Sep
  4. 2024Weak-Mamba-UNet: Visual Mamba Makes CNN and ViT Work Better for Scribble-based Medical Image Segmentation16 Feb
  5. 2024Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation7 Feb
  6. 2024VM-UNet: Vision Mamba UNet for Medical Image Segmentation4 Feb

Other references · 16

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  3. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
  4. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,222
  5. 2024VMamba: Visual State Space Modelcited by 969
  6. 2019UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentationcited by 586
  7. 2023Dynamic Snake Convolution based on Topological Geometric Constraints for Tubular Structure Segmentationcited by 84
  8. 2024ZigMa: A DiT-style Zigzag Mamba Diffusion Modelcited by 84
  9. 2024Vision Mamba: A Comprehensive Survey and Taxonomycited by 54
  10. 2020CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imagingcited by 53
  11. 2022AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in Ultrasound Imagescited by 43
  12. 2023CMUNeXt: An Efficient Medical Image Segmentation Network based on Large Kernel and Skip Fusioncited by 31
4 more
  1. 2022Local Intensity Order Transformation for Robust Curvilinear Object Segmentationcited by 12
  2. 2025Decoding with Structured Awareness: Integrating Directional, Frequency-Spatial, and Structural Attention for Medical Image Segmentationcited by 9
  3. 2025U-Shape Mamba: State Space Model for faster diffusioncited by 5
  4. 2023Curvilinear object segmentation in medical images based on ODoS filter and deep learning networkcited by 3

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  1. 2026U-PEN Mamba: Progressive Expansion with Selective State-Space Modeling for Efficient Retinal Vessel Segmentation
  2. 2025A Novel Hybrid Approach for Retinal Vessel Segmentation with Dynamic Long-Range Dependency and Multi-Scale Retinal Edge Fusion Enhancement
  3. 2025MM-UNet: Morph Mamba U-shaped Convolutional Networks for Retinal Vessel Segmentation
  4. 2026VFGS-Net: Frequency-Guided State-Space Learning for Topology-Preserving Retinal Vessel Segmentation
  5. 2026An Ultra-Widefield Swept-Source OCTA Dataset and a Polar-Gated Mamba Network for Retinal Vessel Segmentation
  6. 2025Joint-octamamba:an octa joint segmentation network based on feature enhanced mamba
  7. 2026RetiWave-Mamba: A Dual-Stream Network for Retinal Disease Detection based on Multi-scale Context and Frequency-Adaptive Mamba Projection
  8. 2025COMMA: Coordinate-aware Modulated Mamba Network for 3D Dispersed Vessel Segmentation