Cargando…
Bi-DCNet: Bilateral Network with Dilated Convolutions for Left Ventricle Segmentation
Left ventricular segmentation is a vital and necessary procedure for assessing cardiac systolic and diastolic function, while echocardiography is an indispensable diagnostic technique that enables cardiac functionality assessment. However, manually labeling the left ventricular region on echocardiog...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10144960/ https://www.ncbi.nlm.nih.gov/pubmed/37109569 http://dx.doi.org/10.3390/life13041040 |
_version_ | 1785034219106336768 |
---|---|
author | Ye, Zi Kumar, Yogan Jaya Song, Fengyan Li, Guanxi Zhang, Suyu |
author_facet | Ye, Zi Kumar, Yogan Jaya Song, Fengyan Li, Guanxi Zhang, Suyu |
author_sort | Ye, Zi |
collection | PubMed |
description | Left ventricular segmentation is a vital and necessary procedure for assessing cardiac systolic and diastolic function, while echocardiography is an indispensable diagnostic technique that enables cardiac functionality assessment. However, manually labeling the left ventricular region on echocardiography images is time consuming and leads to observer bias. Recent research has demonstrated that deep learning has the capability to employ the segmentation process automatically. However, on the downside, it still ignores the contribution of all semantic information through the segmentation process. This study proposes a deep neural network architecture based on BiSeNet, named Bi-DCNet. This model comprises a spatial path and a context path, with the former responsible for spatial feature (low-level) acquisition and the latter responsible for contextual semantic feature (high-level) exploitation. Moreover, it incorporates feature extraction through the integration of dilated convolutions to achieve a larger receptive field to capture multi-scale information. The EchoNet-Dynamic dataset was utilized to assess the proposed model, and this is the first bilateral-structured network implemented on this large clinical video dataset for accomplishing the segmentation of the left ventricle. As demonstrated by the experimental outcomes, our method obtained 0.9228 and 0.8576 in DSC and IoU, respectively, proving the structure’s effectiveness. |
format | Online Article Text |
id | pubmed-10144960 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-101449602023-04-29 Bi-DCNet: Bilateral Network with Dilated Convolutions for Left Ventricle Segmentation Ye, Zi Kumar, Yogan Jaya Song, Fengyan Li, Guanxi Zhang, Suyu Life (Basel) Article Left ventricular segmentation is a vital and necessary procedure for assessing cardiac systolic and diastolic function, while echocardiography is an indispensable diagnostic technique that enables cardiac functionality assessment. However, manually labeling the left ventricular region on echocardiography images is time consuming and leads to observer bias. Recent research has demonstrated that deep learning has the capability to employ the segmentation process automatically. However, on the downside, it still ignores the contribution of all semantic information through the segmentation process. This study proposes a deep neural network architecture based on BiSeNet, named Bi-DCNet. This model comprises a spatial path and a context path, with the former responsible for spatial feature (low-level) acquisition and the latter responsible for contextual semantic feature (high-level) exploitation. Moreover, it incorporates feature extraction through the integration of dilated convolutions to achieve a larger receptive field to capture multi-scale information. The EchoNet-Dynamic dataset was utilized to assess the proposed model, and this is the first bilateral-structured network implemented on this large clinical video dataset for accomplishing the segmentation of the left ventricle. As demonstrated by the experimental outcomes, our method obtained 0.9228 and 0.8576 in DSC and IoU, respectively, proving the structure’s effectiveness. MDPI 2023-04-18 /pmc/articles/PMC10144960/ /pubmed/37109569 http://dx.doi.org/10.3390/life13041040 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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/). |
spellingShingle | Article Ye, Zi Kumar, Yogan Jaya Song, Fengyan Li, Guanxi Zhang, Suyu Bi-DCNet: Bilateral Network with Dilated Convolutions for Left Ventricle Segmentation |
title | Bi-DCNet: Bilateral Network with Dilated Convolutions for Left Ventricle Segmentation |
title_full | Bi-DCNet: Bilateral Network with Dilated Convolutions for Left Ventricle Segmentation |
title_fullStr | Bi-DCNet: Bilateral Network with Dilated Convolutions for Left Ventricle Segmentation |
title_full_unstemmed | Bi-DCNet: Bilateral Network with Dilated Convolutions for Left Ventricle Segmentation |
title_short | Bi-DCNet: Bilateral Network with Dilated Convolutions for Left Ventricle Segmentation |
title_sort | bi-dcnet: bilateral network with dilated convolutions for left ventricle segmentation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10144960/ https://www.ncbi.nlm.nih.gov/pubmed/37109569 http://dx.doi.org/10.3390/life13041040 |
work_keys_str_mv | AT yezi bidcnetbilateralnetworkwithdilatedconvolutionsforleftventriclesegmentation AT kumaryoganjaya bidcnetbilateralnetworkwithdilatedconvolutionsforleftventriclesegmentation AT songfengyan bidcnetbilateralnetworkwithdilatedconvolutionsforleftventriclesegmentation AT liguanxi bidcnetbilateralnetworkwithdilatedconvolutionsforleftventriclesegmentation AT zhangsuyu bidcnetbilateralnetworkwithdilatedconvolutionsforleftventriclesegmentation |