An Overview of Deep Learning Methods for Left Ventricle Segmentation. (30th January 2023)

• Record Type: Journal Article
• Authors: Shoaib, Muhammad Ali; Chuah, Joon Huang; Ali, Raza; Hasikin, Khairunnisa; Khalil, Azira; Hum, Yan Chai; Tee, Yee Kai; Dhanalakshmi, Samiappan; Lai, Khin Wee
• Other Names: Ning Xin Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2023(2023)
• Publication Date: 2023-01-30
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2023/4208231
• Languages: English
• ISSNs: 1687-5265

Abstract

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.