TY - JOUR
T1 - Atherosclerotic Plaque Segmentation Based on Strain Gradients
T2 - A Theoretical Framework
AU - Latorre, Álvaro T.
AU - Martínez, Miguel A.
AU - Cilla, Myriam
AU - Ohayon, Jacques
AU - Peña, Estefanía
N1 - Funding Information:
This work was supported by the Spanish Ministry of Science and Technology through research project PID2019-107517RB-I00, the regional Government of Aragón support for the funding of the research project T24-20R, and grant CUS/581/2020. CIBER Actions are financed by the Instituto de Salud Carlos III with assistance from the European Regional Development Fund. Jacques Ohayon was supported by the SIMR project (2019–2023) operated by the French National Research Agency.
Publisher Copyright:
© 2022 by the authors.
PY - 2022/11
Y1 - 2022/11
N2 - Background: 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 (Formula presented.), (Formula presented.) or (Formula presented.). 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.
AB - Background: 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 (Formula presented.), (Formula presented.) or (Formula presented.). 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.
KW - atherosclerosis
KW - fibrous cap thickness
KW - finite element model
KW - intravascular ultrasound
KW - segmentation method
KW - strain gradient
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U2 - 10.3390/math10214020
DO - 10.3390/math10214020
M3 - Article
AN - SCOPUS:85141841404
VL - 10
JO - Mathematics
JF - Mathematics
SN - 2227-7390
IS - 21
M1 - 4020
ER -