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Related Concept Videos

Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

286
Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
286

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Optimising Generalisable Deep Learning Models for CT Coronary Segmentation: A Multifactorial Evaluation.

Shisheng Zhang1, Ramtin Gharleghi2, Sonit Singh3

  • 1School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, Australia. shisheng.zhang@unsw.edu.au.

Journal of Imaging Informatics in Medicine
|September 19, 2025
PubMed
Summary
This summary is machine-generated.

Deep learning models for coronary artery segmentation show improved accuracy with better image contrast and sharpness, but calcification negatively impacts performance. Findings highlight the need to account for imaging characteristics and vessel anatomy for robust CAD management.

Keywords:
CTCAComputed tomography coronary angiographyConvolutional neural networksCoronary artery segmentationDeep learningModel generalisability

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Cardiovascular Disease

Background:

  • Coronary artery disease (CAD) is a major global health concern, driving the need for advanced diagnostic tools.
  • Automated medical image segmentation, particularly using deep learning, offers potential for improved CAD management and diagnosis.
  • Current deep learning models face challenges in achieving consistent performance across diverse datasets due to variations in imaging and patient factors.

Purpose of the Study:

  • To investigate the impact of image quality and resolution on coronary artery segmentation accuracy using deep learning.
  • To evaluate how factors like vessel size, calcification, contrast enhancement, and edge sharpness influence segmentation performance.
  • To provide a data-driven foundation for developing more generalisable deep learning models for coronary artery segmentation.

Main Methods:

  • Utilized two datasets (ASOCA and GeoCAD) for training and validation of deep learning models.
  • Implemented and compared three deep learning architectures: U-Net, Swin-UNETR, and EfficientNet-LinkNet.
  • Assessed the influence of imaging characteristics (contrast-to-noise ratio, artery contrast enhancement, edge sharpness) and calcification extent on segmentation accuracy.

Main Results:

  • Artery contrast enhancement (r=0.408, p<0.001) and edge sharpness (r=0.239, p=0.046) significantly correlated with improved segmentation.
  • Calcification negatively impacted segmentation accuracy across all severity levels, with low calcification posing the most significant challenge (p<0.05).
  • Larger vessel diameters (OM1 in males, LM and RCA in females) were associated with better segmentation performance for those specific vessels.

Conclusions:

  • Image quality metrics like contrast enhancement and edge sharpness are crucial for accurate coronary artery segmentation.
  • Calcification presents a significant obstacle to segmentation accuracy, necessitating targeted algorithmic improvements.
  • Accounting for anatomical variability, such as vessel diameter, is essential for enhancing the generalizability of deep learning models in CAD analysis.