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Related Experiment Video

Updated: Jun 20, 2026

Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging
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Deep learning based coronary vessels segmentation in X-ray angiography using temporal information.

Haorui He1, Abhirup Banerjee2, Robin P Choudhury3

  • 1Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, United Kingdom.

Medical Image Analysis
|March 6, 2025
PubMed
Summary
This summary is machine-generated.

A new Temporal Vessel Segmentation Network (TVS-Net) improves automated coronary artery segmentation in invasive coronary angiography (ICA) by fusing sequential images. This method enhances diagnostic accuracy for cardiac interventions.

Keywords:
Coronary vessels segmentationNested encoder decoderTemporal informationVessel connectivityX-ray coronary angiography

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Cardiovascular Interventions

Background:

  • Invasive coronary angiography (ICA) is crucial for cardiac interventions, requiring precise coronary vessel segmentation for diagnosis and treatment planning.
  • Existing automated segmentation methods struggle with ICA challenges like motion artifacts, contrast variations, and overlapping organ shadows inherent in X-ray imaging.

Purpose of the Study:

  • To develop and evaluate a novel deep learning model, the Temporal Vessel Segmentation Network (TVS-Net), for accurate and robust coronary vessel segmentation in multi-frame ICA.
  • To address limitations of current automated segmentation techniques by fusing sequential ICA data within a unique 3D encoder-2D decoder architecture.

Main Methods:

  • Development of TVS-Net, a model integrating sequential ICA information using a densely connected 3D encoder-2D decoder structure.
  • Training and validation on a dataset of 323 ICA samples, employing a relaxed annotation protocol for coarse-grained segmentation.
  • Utilizing a loss function based on elastic interaction to enhance segmentation accuracy.

Main Results:

  • Achieved 83.4% Dice score and 84.3% recall on the primary test dataset using coarse-grained annotations.
  • Demonstrated superior performance on an external dataset from a local hospital (78.5% Dice, 82.4% recall), outperforming state-of-the-art methods.
  • Attained high scores (86.2% Dice, 86.3% recall) on a strictly annotated subset, validating the network's effectiveness and robustness.

Conclusions:

  • TVS-Net effectively segments coronary vessels in multi-frame ICA, proving generalizable and robust across different settings.
  • The study highlights the feasibility of using weak supervision for coronary vessel segmentation in ICA.
  • The developed model shows significant potential for improving diagnostic and treatment planning in cardiac interventions.