000120239 001__ 120239
000120239 005__ 20240319081026.0
000120239 0247_ $$2doi$$a10.3390/math10214020
000120239 0248_ $$2sideral$$a131161
000120239 037__ $$aART-2022-131161
000120239 041__ $$aeng
000120239 100__ $$aLatorre, Álvaro T.$$uUniversidad de Zaragoza
000120239 245__ $$aAtherosclerotic Plaque Segmentation Based on Strain Gradients: A Theoretical Framework
000120239 260__ $$c2022
000120239 5060_ $$aAccess copy available to the general public$$fUnrestricted
000120239 5203_ $$aBackground: Atherosclerotic plaque detection is a clinical and technological problem that has been approached by different studies. Nowadays, intravascular ultrasound (IVUS) is the standard used to capture images of the coronary walls and to detect plaques. However, IVUS images are difficult to segment, which complicates obtaining geometric measurements of the plaque. Objective: IVUS, in combination with new techniques, allows estimation of strains in the coronary section. In this study, we have proposed the use of estimated strains to develop a methodology for plaque segmentation. Methods: The process is based on the representation of strain gradients and the combination of the Watershed and Gradient Vector Flow algorithms. Since it is a theoretical framework, the methodology was tested with idealized and real IVUS geometries. Results: We achieved measurements of the lipid area and fibrous cap thickness, which are essential clinical information, with promising results. The success of the segmentation depends on the plaque geometry and the strain gradient variable (SGV) that was selected. However, there are some SGV combinations that yield good results regardless of plaque geometry such as ▽εvMises+▽εrθ, ▽εyy+▽εrr or ▽εmin+▽εTresca. These combinations of SGVs achieve good segmentations, with an accuracy between 97.10% and 94.39% in the best pairs. Conclusions: The new methodology provides fast segmentation from different strain variables, without an optimization step.
000120239 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FSE/T24-20R$$9info:eu-repo/grantAgreement/ES/MICINN/PID2019-107517RB-I00
000120239 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000120239 590__ $$a2.4$$b2022
000120239 592__ $$a0.446$$b2022
000120239 591__ $$aMATHEMATICS$$b23 / 329 = 0.07$$c2022$$dQ1$$eT1
000120239 593__ $$aComputer Science (miscellaneous)$$c2022$$dQ2
000120239 593__ $$aMathematics (miscellaneous)$$c2022$$dQ2
000120239 593__ $$aEngineering (miscellaneous)$$c2022$$dQ2
000120239 594__ $$a3.5$$b2022
000120239 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000120239 700__ $$0(orcid)0000-0002-8375-0354$$aMartínez, Miguel A.$$uUniversidad de Zaragoza
000120239 700__ $$0(orcid)0000-0002-8503-9291$$aCilla, Myriam$$uUniversidad de Zaragoza
000120239 700__ $$aOhayon, Jacques
000120239 700__ $$0(orcid)0000-0002-0664-5024$$aPeña, Estefanía$$uUniversidad de Zaragoza
000120239 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000120239 773__ $$g10, 21 (2022), 4020 [20 pp.]$$pMathematics (Basel)$$tMathematics$$x2227-7390
000120239 8564_ $$s3535932$$uhttps://zaguan.unizar.es/record/120239/files/texto_completo.pdf$$yVersión publicada
000120239 8564_ $$s2697379$$uhttps://zaguan.unizar.es/record/120239/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000120239 909CO $$ooai:zaguan.unizar.es:120239$$particulos$$pdriver
000120239 951__ $$a2024-03-18-16:42:19
000120239 980__ $$aARTICLE