Localized growth distribution on the abdominal aortic aneurysm surface using deep learning approaches

• Published in: E3S web of conferences Vol. 459; p. 2006
• Main Authors: Borisova, Kseniia, Fedotova, Yana, Karpenko, Andrey, Mullyadzhanov, Rustam
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 01.01.2023
• Subjects: Aorta; Aortic aneurysms; Deep learning; Deformation; Formability; Growth rate; Image registration; Medical imaging; Neural networks
• ISSN: 2267-1242; 2555-0403; 2267-1242
• DOI: 10.1051/e3sconf/202345902006

An abdominal aortic aneurysm (AAA) is a dangerous pathology that needs regular monitoring based on medical images. Currently, only visual estimates of the growth rate and methods based on the assessment of changes in the maximum diameter of the aneurysm in clinical practice are used. However, the quantitative assessment of vessel wall growth rate based on deformable image registration is gaining popularity in research. This paper presents a study of the applicability of the neural network approach of image registration for the quantitative growth assessment problem. In this study, we analyzed classical and neural network methods of image registration and used VoxelMorph and HyperMorph neural network architectures to evaluate local AAA growth based on the available dataset. Also, we compared the results of the obtained maximum local deformations of the AAA with the method of estimating the change in the maximum diameter.