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Published on: December 15, 2023
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.
Insights
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.
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.
Abstract:
Purpose.Cerebrovascular segmentation is crucial for the diagnosis and treatment of cerebrovascular diseases. However, accurately extracting cerebral vessels from Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) remains challenging due to the topological complexity and anatomical variability.Methods.This paper presents a novel Y-shaped segmentation network with fast Fourier convolution and Mamba, termed F-Mamba-YNet. The network employs a dual-encoder architecture that effectively leverages the complementarity of spectral and spatial domains for achieving the fusion of multi-level features. The spectral encoder features the Fast Fourier Convolution Module, which captures high-frequency changes in vessel edges, improving segmentation completeness and connectivity. The spatial encoder incorporates a Spatial Mamba Module, which captures long-range dependencies while enhancing the spatial feature representation of cerebral vessels. Additionally, a Multi-scale Feature Selection Module in the decoder adaptively enhances discriminative features, enabling improved feature reuse.Results.Experiments demonstrate that the proposed F-Mamba-YNet achieved 86.28% and 72.24% Dice Similarity Coefficient (DSC) on the IXI-A-SegAN dataset and MIDAS dataset.Conclusions.Compared with existing algorithms, F-Mamba-YNet provided more connected and continuous segmentation results and achieved competitive performance in terms of generalization.
