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Related Experiment Video

Updated: Aug 2, 2025

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LIVE-Net: Comprehensive 3D vessel extraction framework in CT angiography.

Qi Sun1, Jinzhu Yang1, Sizhe Zhao1

  • 1Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China; School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China.

Computers in Biology and Medicine
|April 16, 2023
PubMed
Summary

This study introduces LIVE-Net, a new framework for 3D vessel segmentation and centerline tracking in computed tomography angiography (CTA). LIVE-Net achieves superior accuracy and efficiency, showing significant potential for clinical use in diagnosing vascular diseases.

Keywords:
Attention-embedded atrous residual convolutionCT angiographyComprehensive extraction frameworkDeep learningDual dataflow pathwaysIterative tracking and segmentationMulti-order self-attention U-shape network

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Biomedical Engineering

Background:

  • Vessel extraction from computed tomography angiography (CTA) is crucial for diagnosing vascular diseases.
  • Current methods are laborious, time-consuming, and prone to errors due to anatomical complexity and data characteristics.
  • Accurate 3D vessel segmentation and centerline tracking are essential for reliable clinical diagnosis.

Purpose of the Study:

  • To propose a novel comprehensive vessel extraction framework, LIVE-Net (Local Iterative-based Vessel Extraction Network).
  • To achieve accurate 3D vessel segmentation and simultaneous vessel centerline tracking.
  • To improve the efficiency and accuracy of vessel analysis in CTA for clinical applications.

Main Methods:

  • LIVE-Net utilizes dual dataflow pathways: an iterative tracking network and a local segmentation network.
  • The tracking network employs an attention-embedded atrous pyramid network (aAPN) for precise direction and radius prediction.
  • The segmentation network uses a multi-order self-attention U-shape network (MOSA-UNet) for 3D vascular lumen segmentation.

Main Results:

  • LIVE-Net demonstrated superior performance on both the CAT08 and head and neck CTA datasets.
  • Tracking accuracy metrics included high overlap (e.g., 95.2% on CAT08) and low error distance (0.21 mm).
  • Segmentation achieved high accuracy (DSC: 90.03%, IoU: 81.97%) with remarkable efficiency (67.25s).

Conclusions:

  • LIVE-Net offers a significant advancement in 3D vessel segmentation and centerline tracking from CTA.
  • The network's high accuracy and efficiency indicate strong potential for clinical utility in vascular disease diagnosis.
  • LIVE-Net outperforms state-of-the-art methods, providing more reliable and faster vessel analysis.