EF-net: Accurate edge segmentation for segmenting COVID-19 lung infections from CT images
Despite advances in modern medicine including the use of computed tomography for detecting COVID-19, precise identification and segmentation of lesions remain a significant challenge owing to indistinct boundaries and low degrees of contrast between infected and healthy lung tissues. This study intr...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Heliyon |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402416611X |
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| author | Wenjin Zhong Hanwen Zhang |
| author_facet | Wenjin Zhong Hanwen Zhang |
| author_sort | Wenjin Zhong |
| collection | DOAJ |
| description | Despite advances in modern medicine including the use of computed tomography for detecting COVID-19, precise identification and segmentation of lesions remain a significant challenge owing to indistinct boundaries and low degrees of contrast between infected and healthy lung tissues. This study introduces a novel model called the edge-based dual-parallel attention (EDA)-guided feature-filtering network (EF-Net), specifically designed to accurately segment the edges of COVID-19 lesions. The proposed model comprises two modules: an EDA module and a feature-filtering module (FFM). EDA efficiently extracts structural and textural features from low-level features, enabling the precise identification of lesion boundaries. FFM receives semantically rich features from a deep-level encoder and integrates features with abundant texture and contour information obtained from the EDA module. After filtering through a gating mechanism of the FFM, the EDA features are fused with deep-level features, yielding features rich in both semantic and textural information. Experiments demonstrate that our model outperforms existing models including Inf_Net, GFNet, and BSNet considering various metrics, offering better and clearer segmentation results, particularly for segmenting lesion edges. Moreover, superior performance on the three datasets is achieved, with dice coefficients of 98.1, 97.3, and 72.1 %. |
| format | Article |
| id | doaj-art-399f13d86e5441e59c1cb0cd615d6980 |
| institution | OA Journals |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-399f13d86e5441e59c1cb0cd615d69802025-08-20T01:59:38ZengElsevierHeliyon2405-84402024-12-011023e4058010.1016/j.heliyon.2024.e40580EF-net: Accurate edge segmentation for segmenting COVID-19 lung infections from CT imagesWenjin Zhong0Hanwen Zhang1Corresponding author.; University of New South Wales, AustraliaUniversity of New South Wales, AustraliaDespite advances in modern medicine including the use of computed tomography for detecting COVID-19, precise identification and segmentation of lesions remain a significant challenge owing to indistinct boundaries and low degrees of contrast between infected and healthy lung tissues. This study introduces a novel model called the edge-based dual-parallel attention (EDA)-guided feature-filtering network (EF-Net), specifically designed to accurately segment the edges of COVID-19 lesions. The proposed model comprises two modules: an EDA module and a feature-filtering module (FFM). EDA efficiently extracts structural and textural features from low-level features, enabling the precise identification of lesion boundaries. FFM receives semantically rich features from a deep-level encoder and integrates features with abundant texture and contour information obtained from the EDA module. After filtering through a gating mechanism of the FFM, the EDA features are fused with deep-level features, yielding features rich in both semantic and textural information. Experiments demonstrate that our model outperforms existing models including Inf_Net, GFNet, and BSNet considering various metrics, offering better and clearer segmentation results, particularly for segmenting lesion edges. Moreover, superior performance on the three datasets is achieved, with dice coefficients of 98.1, 97.3, and 72.1 %.http://www.sciencedirect.com/science/article/pii/S240584402416611X |
| spellingShingle | Wenjin Zhong Hanwen Zhang EF-net: Accurate edge segmentation for segmenting COVID-19 lung infections from CT images Heliyon |
| title | EF-net: Accurate edge segmentation for segmenting COVID-19 lung infections from CT images |
| title_full | EF-net: Accurate edge segmentation for segmenting COVID-19 lung infections from CT images |
| title_fullStr | EF-net: Accurate edge segmentation for segmenting COVID-19 lung infections from CT images |
| title_full_unstemmed | EF-net: Accurate edge segmentation for segmenting COVID-19 lung infections from CT images |
| title_short | EF-net: Accurate edge segmentation for segmenting COVID-19 lung infections from CT images |
| title_sort | ef net accurate edge segmentation for segmenting covid 19 lung infections from ct images |
| url | http://www.sciencedirect.com/science/article/pii/S240584402416611X |
| work_keys_str_mv | AT wenjinzhong efnetaccurateedgesegmentationforsegmentingcovid19lunginfectionsfromctimages AT hanwenzhang efnetaccurateedgesegmentationforsegmentingcovid19lunginfectionsfromctimages |