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Updated: Jul 20, 2025

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation
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A Computer-Aided Markov Random Field Segmentation Algorithm for Assessing Fetal Ventricular Chambers.

Natarajan Sriraam1, T V Sushma2, S Suresh3

  • 1Centre for Medical Electronics and Computing, MS Ramaiah Institute of Technology, Bangalore 560054, India.

Critical Reviews in Biomedical Engineering
|July 31, 2023
PubMed
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This study evaluated automated segmentation for fetal heart chambers, crucial for detecting congenital heart disease (CHD). The method showed promising results comparable to expert annotations, aiding early diagnosis.

Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Fetal Cardiology

Background:

  • Congenital heart disease (CHD) is the most common birth defect, affecting fetal heart development.
  • Accurate segmentation of fetal cardiac chambers is vital for early detection and intervention.
  • Ultrasound image speckle noise complicates fetal heart chamber segmentation.

Purpose of the Study:

  • To evaluate the performance of an automated segmentation approach for fetal ventricular chambers.
  • To compare automated segmentation results with manual annotations by clinical experts.
  • To assess the effectiveness of probability-based segmentation and Markov Random Field (MRF) methods.

Main Methods:

  • Utilized 837 ultrasonic biometry sequences from various gestations.

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Related Experiment Videos

Last Updated: Jul 20, 2025

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  • Employed automated, probability-based segmentation and Markov Random Field (MRF) techniques.
  • Validated segmentation efficiency using Dice coefficient, True Positive Ratio (TPR), Similarity Ratio (SIR), and Precision (PR).
  • Incorporated ground truth validation through expert clinical annotation on 56% of the data.

    • Main Results:

    • Automated segmentation achieved comparable results to manual annotations.
    • Achieved an average Dice coefficient of 0.68.
    • Reported an average TPR of 0.723, SIR of 0.604, and PR of 0.632.
    • Demonstrated the potential of automated methods in challenging fetal cardiac imaging.

    Conclusions:

    • The automated segmentation technique provides a reliable method for analyzing fetal ventricular chambers.
    • This approach can aid in the early and accurate diagnosis of congenital heart disease.
    • Further development could enhance segmentation accuracy and clinical applicability.