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DC Field | Value | Language |
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dc.contributor.author | Balasubramani, M | - |
dc.contributor.author | Sung, C-W | - |
dc.contributor.author | Hsieh, M-Y | - |
dc.contributor.author | Huang, EP-C | - |
dc.contributor.author | Shieh, J-S | - |
dc.contributor.author | Abbod, MF | - |
dc.date.accessioned | 2024-08-23T10:59:27Z | - |
dc.date.available | 2024-08-23T10:59:27Z | - |
dc.date.issued | 2024-07-01 | - |
dc.identifier | ORCiD: Madankumar Balasubramani https://orcid.org/0009-0001-8805-3523 | - |
dc.identifier | ORCiD: Chih-Wei Sung https://orcid.org/0000-0003-3312-2752 | - |
dc.identifier | ORCiD: Maysam F. Abbod https://orcid.org/0000-0002-8515-7933 | - |
dc.identifier | 2587 | - |
dc.identifier.citation | Balasubramani, M. et al. (2024) 'Automated Left Ventricle Segmentation in Echocardiography Using YOLO: A Deep Learning Approach for Enhanced Cardiac Function Assessment', Electronics (Switzerland), 13 (13), 2587, pp. 1 - 19. doi: 10.3390/electronics13132587. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/29601 | - |
dc.description | Data Availability Statement: The data presented in this study are available on request from the corresponding author. | en_US |
dc.description.abstract | Accurate segmentation of the left ventricle (LV) using echocardiogram (Echo) images is essential for cardiovascular analysis. Conventional techniques are labor-intensive and exhibit inter-observer variability. Deep learning has emerged as a powerful tool for automated medical image segmentation, offering advantages in speed and potentially superior accuracy. This study explores the efficacy of employing a YOLO (You Only Look Once) segmentation model for automated LV segmentation in Echo images. YOLO, a cutting-edge object detection model, achieves exceptional speed–accuracy balance through its well-designed architecture. It utilizes efficient dilated convolutional layers and bottleneck blocks for feature extraction while incorporating innovations like path aggregation and spatial attention mechanisms. These attributes make YOLO a compelling candidate for adaptation to LV segmentation in Echo images. We posit that by fine-tuning a pre-trained YOLO-based model on a well-annotated Echo image dataset, we can leverage the model’s strengths in real-time processing and precise object localization to achieve robust LV segmentation. The proposed approach entails fine-tuning a pre-trained YOLO model on a rigorously labeled Echo image dataset. Model performance has been evaluated using established metrics such as mean Average Precision (mAP) at an Intersection over Union (IoU) threshold of 50% (mAP50) with 98.31% and across a range of IoU thresholds from 50% to 95% (mAP50:95) with 75.27%. Successful implementation of YOLO for LV segmentation has the potential to significantly expedite and standardize Echo image analysis. This advancement could translate to improved clinical decision-making and enhanced patient care. | en_US |
dc.description.sponsorship | This research received no external funding. | en_US |
dc.format.extent | 1 - 19 | - |
dc.format.medium | Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | MDPI | en_US |
dc.rights | Copyright © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | left ventricle (LV) | en_US |
dc.subject | echocardiogram | en_US |
dc.subject | deep learning | en_US |
dc.subject | segmentation | en_US |
dc.subject | feature extraction | en_US |
dc.subject | dilated convolution | en_US |
dc.subject | YOLO | en_US |
dc.title | Automated Left Ventricle Segmentation in Echocardiography Using YOLO: A Deep Learning Approach for Enhanced Cardiac Function Assessment | en_US |
dc.type | Article | en_US |
dc.date.dateAccepted | 2024-06-29 | - |
dc.identifier.doi | https://doi.org/10.3390/electronics13132587 | - |
dc.relation.isPartOf | Electronics (Switzerland) | - |
pubs.issue | 13 | - |
pubs.publication-status | Published | - |
pubs.volume | 13 | - |
dc.identifier.eissn | 2079-9292 | - |
dc.rights.license | https://creativecommons.org/licenses/by/4.0/legalcode.en | - |
dc.rights.holder | The authors | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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