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Cerebrovascular segmentation network based on fast fourier convolution and Mamba.

Chaozhi Yang1,2, Mingzhe Cao2, Jinbao Zhu3

  • 1School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, People's Republic of China.

Biomedical Physics & Engineering Express
|September 8, 2025
PubMed
Summary

This study introduces F-Mamba-YNet, a new deep learning model for segmenting cerebral vessels in Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) scans. It improves vessel segmentation accuracy and continuity for better cerebrovascular disease diagnosis.

Keywords:
3D cerebrovascular segmentationMambaTOF-MRAfast fourier convolutionfeature selection

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuroscience

Background:

  • Cerebrovascular segmentation is vital for diagnosing and treating cerebrovascular diseases.
  • Accurate segmentation of cerebral vessels from Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) is challenging due to complex anatomy and topology.

Purpose of the Study:

  • To develop a novel deep learning network, F-Mamba-YNet, for improved cerebrovascular segmentation from TOF-MRA.
  • To enhance the accuracy, completeness, and connectivity of cerebral vessel segmentation.

Main Methods:

  • Proposed F-Mamba-YNet, a Y-shaped network utilizing a dual-encoder architecture with Fast Fourier Convolution and Mamba modules.
  • Spectral encoder captures high-frequency vessel edge details; spatial encoder captures long-range dependencies.
  • Multi-scale Feature Selection Module in the decoder for adaptive feature enhancement and reuse.

Main Results:

  • F-Mamba-YNet achieved a Dice Similarity Coefficient (DSC) of 86.28% on the IXI-A-SegAN dataset.
  • Achieved a DSC of 72.24% on the MIDAS dataset.
  • Demonstrated more connected and continuous segmentation results compared to existing methods.

Conclusions:

  • F-Mamba-YNet offers competitive performance and superior segmentation continuity for cerebral vessels.
  • The proposed network shows strong generalization capabilities for cerebrovascular segmentation tasks.