Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/29601
Title: | Automated Left Ventricle Segmentation in Echocardiography Using YOLO: A Deep Learning Approach for Enhanced Cardiac Function Assessment |
Authors: | Balasubramani, M Sung, C-W Hsieh, M-Y Huang, EP-C Shieh, J-S Abbod, MF |
Keywords: | left ventricle (LV);echocardiogram;deep learning;segmentation;feature extraction;dilated convolution;YOLO |
Issue Date: | 1-Jul-2024 |
Publisher: | MDPI |
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. |
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. |
Description: | Data Availability Statement: The data presented in this study are available on request from the corresponding author. |
URI: | https://bura.brunel.ac.uk/handle/2438/29601 |
DOI: | https://doi.org/10.3390/electronics13132587 |
Other Identifiers: | ORCiD: Madankumar Balasubramani https://orcid.org/0009-0001-8805-3523 ORCiD: Chih-Wei Sung https://orcid.org/0000-0003-3312-2752 ORCiD: Maysam F. Abbod https://orcid.org/0000-0002-8515-7933 2587 |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FullText.pdf | 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/). | 2.83 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License