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Updated: May 30, 2026

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training
09:57

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

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2D/3D fetal cardiac dataset segmentation using a deformable model.

Irving Dindoyal1, Tryphon Lambrou, Jing Deng

  • 1Institute of Surgical Technology and Biomechanics, University of Bern, Stauffacherstrasse 78, Bern, Switzerland. irving.dindoyal@istb.unibe.ch

Medical Physics
|August 24, 2011
PubMed
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This study presents an automated method for segmenting fetal heart chambers using ultrasound, improving 3D cardiac assessments. The novel approach accurately delineates structures, reducing errors and enhancing functional analysis.

Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Fetal Cardiology

Background:

  • Ultrasound imaging of the fetal heart is crucial for assessing cardiac function and structure.
  • Image artifacts, such as signal dropout, pose challenges for accurate fetal cardiac segmentation.
  • Existing methods may struggle with the precise delineation of small fetal cardiac chambers.

Purpose of the Study:

  • To develop an automated method for segmenting the fetal heart.
  • To facilitate accurate 3D assessment of fetal cardiac function and structure.
  • To overcome limitations posed by ultrasound artifacts in fetal cardiac imaging.

Main Methods:

  • A level set deformable model was employed for automatic delineation of fetal cardiac chambers.
  • A novel collision detection term was introduced to penalize the model from growing into adjacent compartments.

Related Experiment Videos

Last Updated: May 30, 2026

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training
09:57

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Published on: January 18, 2021

  • A region-based model allowed simultaneous segmentation of all four chambers from user-defined seed points.

    • Main Results:

    • The algorithm demonstrated accurate segmentation with root mean square errors within 2 mm compared to manual tracings.
    • Penalties prevented boundary intersection at signal dropout walls, improving segmentation integrity.
    • Validation using a physical phantom showed volume segmentation errors within 13%.

    Conclusions:

    • The developed algorithm offers an accurate and automated solution for fetal cardiac segmentation.
    • This method aids in the 3D assessment of fetal cardiac function and structure.
    • The study validates the algorithm's performance against manual tracings and physical phantoms.