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An Overview of Deep Learning Methods for Left Ventricle Segmentation

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dc.contributor.authorMuhammad Ali Shoaiben_US
dc.contributor.authorJoon Huang Chuahen_US
dc.contributor.authorRaza Alien_US
dc.contributor.authorKhairunnisa Hasikinen_US
dc.contributor.authorAzira Khalilen_US
dc.contributor.authorYan Chai Humen_US
dc.contributor.authorYee Kai Teeen_US
dc.contributor.authorSamiappan Dhanalakshmien_US
dc.contributor.authorKhin Wee Laien_US
dc.date.accessioned2024-05-29T02:28:08Z
dc.date.available2024-05-29T02:28:08Z
dc.date.issued2023
dc.date.submitted2023-5-31
dc.descriptionVolume 2023 Special Issue Page (1-26)en_US
dc.description.abstractCardiac 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.en_US
dc.identifier.doi10.1155/2023/4208231
dc.identifier.epage26
dc.identifier.issn1687-5265
dc.identifier.issueSpecial Is
dc.identifier.spage1
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/10742
dc.identifier.volume2023
dc.language.isoen_USen_US
dc.publisherHindawien_US
dc.relation.ispartofComputational Intelligence and Neuroscienceen_US
dc.titleAn Overview of Deep Learning Methods for Left Ventricle Segmentationen_US
dc.typeArticleen_US
dspace.entity.typePublication

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