| Issue |
E3S Web Conf.
Volume 459, 2023
XXXIX Siberian Thermophysical Seminar (STS-39)
|
|
|---|---|---|
| Article Number | 02006 | |
| Number of page(s) | 5 | |
| Section | Convective Flows and Heat Transfer in Single-Phase Media | |
| DOI | https://doi.org/10.1051/e3sconf/202345902006 | |
| Published online | 04 December 2023 | |
Localized growth distribution on the abdominal aortic aneurysm surface using deep learning approaches
Novosibirsk State University, 630090, Novosibirsk, Russia
* Corresponding author: i.antonevich@g.nsu.ru
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.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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