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SARC-UNet: A coronary artery segmentation method based on spatial attention and residual convolution.

Fangxun Bao1, Yongqi Zhao1, Xinyue Zhang1

  • 1School of Mathematics, Shandong University, Jinan, Shandong, 250100, China.

Computer Methods and Programs in Biomedicine
|August 3, 2024
PubMed
Summary

This study introduces SARC-UNet, a novel deep learning model for enhanced coronary artery segmentation in medical images. The method improves accuracy, particularly for small vessels and connectivity, outperforming existing techniques.

Keywords:
Coronary artery segmentationFine blood vesselResidual block improvementSpatial attention module

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Coronary artery segmentation is crucial but challenging due to image quality issues like low light and contrast.
  • Existing methods struggle with noise, vascular breakages, and loss of small vessels in coronary angiography.

Purpose of the Study:

  • To develop an advanced automatic segmentation network for coronary arteries in angiography images.
  • To address limitations of current methods by improving accuracy, vessel connectivity, and small vessel detection.

Main Methods:

  • A UNet-based segmentation network (SARC-UNet) incorporating residual convolution and spatial attention was developed.
  • Low-light image enhancement (LIME) was applied to improve image clarity and contrast.
  • Residual convolution fusion modules (RCFM1, RCFM2) and a location-enhanced spatial attention (LESA) mechanism were integrated.

Main Results:

  • The SARC-UNet method demonstrated strong performance across general segmentation metrics.
  • The approach significantly improved connectivity indicators compared to existing methods.
  • Experimental results showed effective blood vessel segmentation with high accuracy.

Conclusions:

  • The proposed SARC-UNet method surpasses state-of-the-art approaches for coronary artery segmentation.
  • The model excels particularly in segmenting small blood vessels and maintaining vessel connectivity.
  • This work offers a robust solution for improving diagnostic accuracy in cardiovascular imaging.