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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.
Insights
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.
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.
Abstract:
Cardiac health diseases are one of the key causes of death around the globe. The number of heart patients has considerably increased during the pandemic. Therefore, it is crucial to assess and analyze the medical and cardiac images. Deep learning architectures, specifically convolutional neural networks have profoundly become the primary choice for the assessment of cardiac medical images. The left ventricle is a vital part of the cardiovascular system where the boundary and size perform a significant role in the evaluation of cardiac function. Due to automatic segmentation and good promising results, the left ventricle segmentation using deep learning has attracted a lot of attention. This article presents a critical review of deep learning methods used for the left ventricle segmentation from frequently used imaging modalities including magnetic resonance images, ultrasound, and computer tomography. This study also demonstrates the details of the network architecture, software, and hardware used for training along with publicly available cardiac image datasets and self-prepared dataset details incorporated. The summary of the evaluation matrices with results used by different researchers is also presented in this study. Finally, all this information is summarized and comprehended in order to assist the readers to understand the motivation and methodology of various deep learning models, as well as exploring potential solutions to future challenges in LV segmentation.
