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<div><p>Currently, plaque segmentation in Optical Coherence Tomography (OCT) images of coronary arteries is primarily carried out manually by physicians, and the accuracy of existing automatic segmentation techniques needs further improvement. To furnish efficient and precise decision su...
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2025
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| _version_ | 1852019438082064384 |
|---|---|
| author | He Meng (33363) |
| author2 | Ran Zhao (337603) Ying Zhang (40767) Bo Zhang (6559) Cheng Zhang (70708) Di Wang (329735) Jinlu Sun (12539914) |
| author2_role | author author author author author author |
| author_facet | He Meng (33363) Ran Zhao (337603) Ying Zhang (40767) Bo Zhang (6559) Cheng Zhang (70708) Di Wang (329735) Jinlu Sun (12539914) |
| author_role | author |
| dc.creator.none.fl_str_mv | He Meng (33363) Ran Zhao (337603) Ying Zhang (40767) Bo Zhang (6559) Cheng Zhang (70708) Di Wang (329735) Jinlu Sun (12539914) |
| dc.date.none.fl_str_mv | 2025-06-10T17:57:29Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0325911.g002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Workflow_of_this_paper_/29285222 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Cell Biology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified precise decision support outcomes strongly attest optical coherence tomography future research endeavors experimental results demonstrate dense atrous convolution plaque recognition algorithm div >< p coronary artery plaques coronary oct images coronary arteries proposed algorithm plaque segmentation significant resource relatively well realize high primarily carried precision segmentation lipid plaques furnish efficient dice </ dataset serves automated detection attention mechanism |
| dc.title.none.fl_str_mv | Workflow of this paper. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>Currently, plaque segmentation in Optical Coherence Tomography (OCT) images of coronary arteries is primarily carried out manually by physicians, and the accuracy of existing automatic segmentation techniques needs further improvement. To furnish efficient and precise decision support, automated detection of plaques in coronary OCT images holds paramount importance. For addressing these challenges, we propose a novel deep learning algorithm featuring Dense Atrous Convolution (DAC) and attention mechanism to realize high-precision segmentation and classification of Coronary artery plaques. Then, a relatively well-established dataset covering 760 original images, expanded to 8,000 using data enhancement. This dataset serves as a significant resource for future research endeavors. The experimental results demonstrate that the <i>dice</i> coefficients of calcified, fibrous, and lipid plaques are 0.913, 0.900, and 0.879, respectively, surpassing those generated by five other conventional medical image segmentation networks. These outcomes strongly attest to the effectiveness and superiority of our proposed algorithm in the task of automatic coronary artery plaque segmentation.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_f9af1e2a33a4f972f2e4ed8d5cc259ac |
| identifier_str_mv | 10.1371/journal.pone.0325911.g002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29285222 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Workflow of this paper.He Meng (33363)Ran Zhao (337603)Ying Zhang (40767)Bo Zhang (6559)Cheng Zhang (70708)Di Wang (329735)Jinlu Sun (12539914)MedicineCell BiologySpace ScienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedprecise decision supportoutcomes strongly attestoptical coherence tomographyfuture research endeavorsexperimental results demonstratedense atrous convolutionplaque recognition algorithmdiv >< pcoronary artery plaquescoronary oct imagescoronary arteriesproposed algorithmplaque segmentationsignificant resourcerelatively wellrealize highprimarily carriedprecision segmentationlipid plaquesfurnish efficientdice </dataset servesautomated detectionattention mechanism<div><p>Currently, plaque segmentation in Optical Coherence Tomography (OCT) images of coronary arteries is primarily carried out manually by physicians, and the accuracy of existing automatic segmentation techniques needs further improvement. To furnish efficient and precise decision support, automated detection of plaques in coronary OCT images holds paramount importance. For addressing these challenges, we propose a novel deep learning algorithm featuring Dense Atrous Convolution (DAC) and attention mechanism to realize high-precision segmentation and classification of Coronary artery plaques. Then, a relatively well-established dataset covering 760 original images, expanded to 8,000 using data enhancement. This dataset serves as a significant resource for future research endeavors. The experimental results demonstrate that the <i>dice</i> coefficients of calcified, fibrous, and lipid plaques are 0.913, 0.900, and 0.879, respectively, surpassing those generated by five other conventional medical image segmentation networks. These outcomes strongly attest to the effectiveness and superiority of our proposed algorithm in the task of automatic coronary artery plaque segmentation.</p></div>2025-06-10T17:57:29ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0325911.g002https://figshare.com/articles/figure/Workflow_of_this_paper_/29285222CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/292852222025-06-10T17:57:29Z |
| spellingShingle | Workflow of this paper. He Meng (33363) Medicine Cell Biology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified precise decision support outcomes strongly attest optical coherence tomography future research endeavors experimental results demonstrate dense atrous convolution plaque recognition algorithm div >< p coronary artery plaques coronary oct images coronary arteries proposed algorithm plaque segmentation significant resource relatively well realize high primarily carried precision segmentation lipid plaques furnish efficient dice </ dataset serves automated detection attention mechanism |
| status_str | publishedVersion |
| title | Workflow of this paper. |
| title_full | Workflow of this paper. |
| title_fullStr | Workflow of this paper. |
| title_full_unstemmed | Workflow of this paper. |
| title_short | Workflow of this paper. |
| title_sort | Workflow of this paper. |
| topic | Medicine Cell Biology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified precise decision support outcomes strongly attest optical coherence tomography future research endeavors experimental results demonstrate dense atrous convolution plaque recognition algorithm div >< p coronary artery plaques coronary oct images coronary arteries proposed algorithm plaque segmentation significant resource relatively well realize high primarily carried precision segmentation lipid plaques furnish efficient dice </ dataset serves automated detection attention mechanism |