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Deep Learning for Cardiac Image Segmentation: A Review.

Chen Chen¹, Chen Qin¹, Huaqi Qiu¹

¹Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom.

Frontiers in Cardiovascular Medicine

|March 21, 2020

Summary

This summary is machine-generated.

Deep learning is revolutionizing cardiac image segmentation across MRI, CT, and ultrasound. This review covers over 100 studies, highlighting datasets and future research directions for reproducible cardiac imaging analysis.

Keywords:

CT MRI artificial intelligence cardiac image analysis cardiac image segmentation deep learning neural networks ultrasound

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Area of Science:

Medical Imaging
Artificial Intelligence
Cardiology

Background:

Deep learning (DL) is the predominant method for cardiac image segmentation.
Cardiac segmentation is crucial for diagnosing and monitoring heart conditions.

Purpose of the Study:

To review deep learning applications in cardiac image segmentation.
To cover major imaging modalities (MRI, CT, ultrasound) and anatomical structures.
To provide resources for reproducible research.

Main Methods:

Systematic review of over 100 deep learning-based cardiac image segmentation papers.
Analysis of common imaging modalities and anatomical targets.
Compilation of publicly available datasets and code repositories.

Main Results:

Deep learning models are widely applied across various cardiac imaging techniques.
Key anatomical structures like ventricles, atria, and vessels are common segmentation targets.
Publicly available resources are identified to foster research reproducibility.

Conclusions:

Deep learning shows significant promise in cardiac image segmentation.
Challenges include data scarcity, model generalizability, and interpretability.
Future research should address these limitations to advance clinical applications.