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A frequency-spatial dual perception network for efficient and accurate medical image segmentation.

Daxin Chen1, Jiahua Wu2, Xu-Yao Zhang3

  • 1Fujian Key Laboratory of Pattern Recognition and Image Understanding, School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, 361024, China.

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This study introduces FDE-Net, an efficient medical image segmentation network that leverages frequency-domain information to enhance pathological feature extraction. FDE-Net achieves superior accuracy and computational efficiency, showing promise for clinical applications.

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Area of Science:

  • Medical image analysis
  • Computer vision
  • Biomedical engineering

Background:

  • Medical image analysis benefits from frequency-domain information due to differences in image acquisition compared to natural images.
  • Extracting significant pathological features from diverse frequency domains is a key challenge in medical image segmentation.
  • Existing methods struggle to effectively integrate frequency-domain and spatial information for improved segmentation.

Purpose of the Study:

  • To propose an efficient medical image segmentation network, FDE-Net, that effectively utilizes frequency-domain information.
  • To enhance the extraction of discriminative pathological features by selectively processing frequency-domain data.
  • To improve multi-scale spatial feature extraction and integrate it seamlessly with frequency-domain features.

Main Methods:

  • Developed FDE-Net, a U-shaped network incorporating a Low-Frequency Information Extraction Block (LFIEB) to enhance critical frequency-domain features.
  • Integrated a Multi-head Perception Visual State Space (MPVSS) module with structural optimizations for improved multi-scale spatial feature extraction.
  • Incorporated a Context Focus Attention (CFA) module for efficient shallow feature propagation in the decoder.

Main Results:

  • FDE-Net achieved 84.10% IoU and 91.29% DSC on the ISIC-2018 dataset, outperforming UNet.
  • The proposed method demonstrated superior segmentation accuracy while maintaining computational efficiency.
  • Ablation studies confirmed the significant contributions of the LFIEB and MPVSS modules to the network's performance.

Conclusions:

  • FDE-Net effectively utilizes frequency-domain information for enhanced medical image segmentation.
  • The network demonstrates a promising balance between segmentation accuracy and computational efficiency.
  • FDE-Net shows potential for successful clinical deployment in medical image analysis tasks.