Accurate 3D contrast-free myocardial infarction delineation using a 4D dual-stream spatiotemporal feature learning framework

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dc.authorid0000-0003-1840-9958en_US
dc.contributor.authorLiu, Jinhao
dc.contributor.authorZhu, Xinglai
dc.contributor.authorXu, Chenchu
dc.contributor.authorXu, Lei
dc.contributor.authorPolat, Kemal
dc.contributor.authorAlenezi, Fayadh
dc.date.accessioned2024-05-22T13:26:47Z
dc.date.available2024-05-22T13:26:47Z
dc.date.issued2023en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionThis work was supported in part by The National Natural Science Foundation of China (62106001, U1908211), The University Synergy Innovation Program of Anhui Province, China (GXXT-2021-007), and The Anhui Provincial Natural Science Foundation, China (2208085Y19).en_US
dc.description.abstractAccurate 3D contrast-free myocardial infarction (MI) delineation has the potential to eliminate the need for toxic injections, thereby significantly advances diagnosis and treatment of MI. In this study, we propose a 4D dual-stream spatiotemporal feature learning framework (4D-DSS) that enables learning of 4D (3D + T) representation of the heart to accurately map the 3D MI regions, thereby directly delineating of 3D MI without contrast agent. This framework creatively introduces a dual-stream 3D spatiotemporal point cloud architecture enables to learn the myocardial 4D representation in both local and global aspects, and improve the comprehension and precision of the representation. Specifically, the framework utilizes the local spatiotemporal variation of individual point clouds to characterize minute distortions in myocardial regions and the global spatiotemporal variation of point cloud sequences to represent the overall myocardial motion between frames, thereby enables comprehensive learning of 3D myocardial motion and leverages these features to classify myocardial tissue into MI regions and normal regions. 4D-DSS significantly improved performance (with a precision increase of at least 4%) compared to four advanced methods. The results support the impact of our 4D-DSS framework on the development and implementation of 3D contrast-free myocardial infarction region delineation technology.& COPY; 2023 Elsevier B.V. All rights reserved.en_US
dc.description.sponsorshipNational Natural Science Foundation of China [U1908211, GXXT-2021-007]; University Synergy Innovation Program of Anhui Province, China [2208085Y19]; Anhui Provincial Natural Science Foundation, China; [62106001]en_US
dc.identifier.citationLiu, J., Zhu, X., Xu, C., Xu, L., Gao, Z., Polat, K., & Alenezi, F. (2023). Accurate 3D contrast-free myocardial infarction delineation using a 4D dual-stream spatiotemporal feature learning framework. Applied Soft Computing, 146, 110694.en_US
dc.identifier.doi10.1016/j.asoc.2023.110694
dc.identifier.endpage13en_US
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85167461722en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.asoc.2023.110694
dc.identifier.urihttps://hdl.handle.net/20.500.12491/12148
dc.identifier.volume146en_US
dc.identifier.wosWOS:001063346600001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofApplied Soft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMyocardial Infarctionen_US
dc.subjectContrast-freeen_US
dc.subject4D-Spatiotemporal Feature Learningen_US
dc.subjectPoint Clouden_US
dc.subjectScene Flowen_US
dc.titleAccurate 3D contrast-free myocardial infarction delineation using a 4D dual-stream spatiotemporal feature learning frameworken_US
dc.typeArticleen_US

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