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Updated: Aug 19, 2025

Three-Dimensional Printing of a Complex Aortic Anomaly
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Segmentation of human aorta using 3D nnU-net-oriented deep learning.

Feng Li1, Lianzhong Sun1, Kwok-Yan Lam2

  • 1Zhejiang Gongshang University, Hangzhou 310018, China.

The Review of Scientific Instruments
|December 3, 2022
PubMed
Summary

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This summary is machine-generated.

Deep learning using the nnU-Net framework accurately segments cardiac aorta and surrounding tissues in computed tomography angiography (CTA) images. This automated approach improves segmentation accuracy for cardiovascular disease diagnosis and treatment planning.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Cardiovascular Diseases

Background:

  • Computed tomography angiography (CTA) is crucial for diagnosing cardiovascular diseases.
  • Accurate segmentation of the aortic sinus and cardiac tissues is essential for transcatheter aortic valve interventions.
  • Traditional segmentation methods face challenges with accuracy, fuzzy edges, and patient variability.

Purpose of the Study:

  • To develop and validate a deep learning (DL) framework for segmenting cardiac aorta and perivalvular tissues in CTA images.
  • To assess the accuracy and effectiveness of the proposed nnU-Net model for this segmentation task.
  • To address limitations of existing segmentation techniques in clinical practice.

Main Methods:

  • Utilized the nnU-Net (no-new-Net) deep learning framework for automated image segmentation.

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  • Trained and validated the model on 130 sets of cardiac CTA image data (88 training, 22 validation, 20 test).
  • Leveraged nn-Net's capabilities for automatic preprocessing, data augmentation, and dynamic network configuration.

    • Main Results:

    • Achieved high accuracy in segmenting the cardiac aorta and tissues near the aortic valve, with an average Dice similarity coefficient of 0.9698 ± 0.0081.
    • Demonstrated that the DL-based segmentation effectively meets preoperative clinical requirements.
    • The nnU-Net model showed superior performance compared to traditional methods, handling fuzzy edges and patient variations effectively.

    Conclusions:

    • The nnU-Net deep learning model provides an accurate and effective solution for cardiac CTA image segmentation.
    • This automated approach overcomes limitations of traditional segmentation methods, enhancing diagnostic and treatment planning capabilities.
    • nnU-Net shows significant promise as a key DL technology for cardiac CTA image analysis.