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An Overview of Deep Learning Methods for Left Ventricle Segmentation.

Muhammad Ali Shoaib1,2, Joon Huang Chuah1, Raza Ali1,2

  • 1Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.

Computational Intelligence and Neuroscience
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Summary
This summary is machine-generated.

Deep learning models effectively segment the left ventricle (LV) in cardiac images, crucial for assessing heart function. This review details methods, datasets, and challenges in LV segmentation using artificial intelligence.

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Area of Science:

  • Cardiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Biomedical Engineering

Background:

  • Cardiac diseases are a leading global cause of death, with increasing patient numbers.
  • Accurate assessment of cardiac images is vital for patient diagnosis and treatment.
  • The left ventricle's size and boundary are critical indicators of cardiac function.

Purpose of the Study:

  • To critically review deep learning (DL) methods for left ventricle (LV) segmentation.
  • To cover DL applications across various cardiac imaging modalities.
  • To provide a comprehensive resource for understanding LV segmentation techniques.

Main Methods:

  • Review of deep learning architectures, particularly convolutional neural networks (CNNs).
  • Analysis of segmentation techniques applied to cardiac MRI, ultrasound, and CT.
  • Inclusion of details on network architectures, software, hardware, and datasets.

Main Results:

  • Deep learning shows promising results for automatic LV segmentation.
  • Various evaluation metrics and their outcomes are summarized.
  • The study compiles information on publicly available and custom datasets.

Conclusions:

  • Deep learning is a powerful tool for LV segmentation in cardiac imaging.
  • Understanding DL methodologies is key to advancing cardiac function assessment.
  • The review identifies future challenges and potential solutions in LV segmentation.