Staff View: Fully automatic left ventricle segmentation using bilateral lightweight deep neural network

Fully automatic left ventricle segmentation using bilateral lightweight deep neural network

The segmentation of the left ventricle (LV) is one of the fundamental procedures that must be performed to obtain quantitative measures of the heart, such as its volume, area, and ejection fraction. In clinical practice, the delineation of LV is still often conducted semi-automatically, leaving it o...

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Main Authors:	Shoaib, Muhammad Ali, Chuah, Joon Huang, Ali, Raza, Dhanalakshmi, Samiappan, Hum, Yan Chai, Khalil, Azira, Lai, Khin Wee
Format:	Article
Published:	MDPI 2023
Subjects:	T Technology (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.um.edu.my/39110/
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spelling	my.um.eprints.391102024-11-24T04:19:50Z http://eprints.um.edu.my/39110/ Fully automatic left ventricle segmentation using bilateral lightweight deep neural network Shoaib, Muhammad Ali Chuah, Joon Huang Ali, Raza Dhanalakshmi, Samiappan Hum, Yan Chai Khalil, Azira Lai, Khin Wee T Technology (General) TK Electrical engineering. Electronics Nuclear engineering The segmentation of the left ventricle (LV) is one of the fundamental procedures that must be performed to obtain quantitative measures of the heart, such as its volume, area, and ejection fraction. In clinical practice, the delineation of LV is still often conducted semi-automatically, leaving it open to operator subjectivity. The automatic LV segmentation from echocardiography images is a challenging task due to poorly defined boundaries and operator dependency. Recent research has demonstrated that deep learning has the capability to employ the segmentation process automatically. However, the well-known state-of-the-art segmentation models still lack in terms of accuracy and speed. This study aims to develop a single-stage lightweight segmentation model that precisely and rapidly segments the LV from 2D echocardiography images. In this research, a backbone network is used to acquire both low-level and high-level features. Two parallel blocks, known as the spatial feature unit and the channel feature unit, are employed for the enhancement and improvement of these features. The refined features are merged by an integrated unit to segment the LV. The performance of the model and the time taken to segment the LV are compared to other established segmentation models, DeepLab, FCN, and Mask RCNN. The model achieved the highest values of the dice similarity index (0.9446), intersection over union (0.8445), and accuracy (0.9742). The evaluation metrics and processing time demonstrate that the proposed model not only provides superior quantitative results but also trains and segments the LV in less time, indicating its improved performance over competing segmentation models. MDPI 2023-01 Article PeerReviewed Shoaib, Muhammad Ali and Chuah, Joon Huang and Ali, Raza and Dhanalakshmi, Samiappan and Hum, Yan Chai and Khalil, Azira and Lai, Khin Wee (2023) Fully automatic left ventricle segmentation using bilateral lightweight deep neural network. Life, 13 (1). ISSN 20751729, DOI https://doi.org/10.3390/life13010124 <https://doi.org/10.3390/life13010124>. 10.3390/life13010124
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	T Technology (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Shoaib, Muhammad Ali Chuah, Joon Huang Ali, Raza Dhanalakshmi, Samiappan Hum, Yan Chai Khalil, Azira Lai, Khin Wee Fully automatic left ventricle segmentation using bilateral lightweight deep neural network
description	The segmentation of the left ventricle (LV) is one of the fundamental procedures that must be performed to obtain quantitative measures of the heart, such as its volume, area, and ejection fraction. In clinical practice, the delineation of LV is still often conducted semi-automatically, leaving it open to operator subjectivity. The automatic LV segmentation from echocardiography images is a challenging task due to poorly defined boundaries and operator dependency. Recent research has demonstrated that deep learning has the capability to employ the segmentation process automatically. However, the well-known state-of-the-art segmentation models still lack in terms of accuracy and speed. This study aims to develop a single-stage lightweight segmentation model that precisely and rapidly segments the LV from 2D echocardiography images. In this research, a backbone network is used to acquire both low-level and high-level features. Two parallel blocks, known as the spatial feature unit and the channel feature unit, are employed for the enhancement and improvement of these features. The refined features are merged by an integrated unit to segment the LV. The performance of the model and the time taken to segment the LV are compared to other established segmentation models, DeepLab, FCN, and Mask RCNN. The model achieved the highest values of the dice similarity index (0.9446), intersection over union (0.8445), and accuracy (0.9742). The evaluation metrics and processing time demonstrate that the proposed model not only provides superior quantitative results but also trains and segments the LV in less time, indicating its improved performance over competing segmentation models.
format	Article
author	Shoaib, Muhammad Ali Chuah, Joon Huang Ali, Raza Dhanalakshmi, Samiappan Hum, Yan Chai Khalil, Azira Lai, Khin Wee
author_facet	Shoaib, Muhammad Ali Chuah, Joon Huang Ali, Raza Dhanalakshmi, Samiappan Hum, Yan Chai Khalil, Azira Lai, Khin Wee
author_sort	Shoaib, Muhammad Ali
title	Fully automatic left ventricle segmentation using bilateral lightweight deep neural network
title_short	Fully automatic left ventricle segmentation using bilateral lightweight deep neural network
title_full	Fully automatic left ventricle segmentation using bilateral lightweight deep neural network
title_fullStr	Fully automatic left ventricle segmentation using bilateral lightweight deep neural network
title_full_unstemmed	Fully automatic left ventricle segmentation using bilateral lightweight deep neural network
title_sort	fully automatic left ventricle segmentation using bilateral lightweight deep neural network
publisher	MDPI
publishDate	2023
url	http://eprints.um.edu.my/39110/
_version_	1817841942606643200
score	13.222552

Fully automatic left ventricle segmentation using bilateral lightweight deep neural network

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