Standard

Localized growth distribution on the abdominal aortic aneurysm surface using deep learning approaches. / Borisova, Kseniia; Fedotova, Yana; Karpenko, Andrey et al.

In: E3S Web of Conferences, Vol. 459, 02006, 04.12.2023.

Research output: Contribution to journal › Conference article › peer-review

Harvard

Borisova, K, Fedotova, Y, Karpenko, A & Mullyadzhanov, R 2023, 'Localized growth distribution on the abdominal aortic aneurysm surface using deep learning approaches', E3S Web of Conferences, vol. 459, 02006. https://doi.org/10.1051/e3sconf/202345902006

APA

Borisova, K., Fedotova, Y., Karpenko, A., & Mullyadzhanov, R. (2023). Localized growth distribution on the abdominal aortic aneurysm surface using deep learning approaches. E3S Web of Conferences, 459, [02006]. https://doi.org/10.1051/e3sconf/202345902006

Vancouver

Borisova K, Fedotova Y, Karpenko A, Mullyadzhanov R. Localized growth distribution on the abdominal aortic aneurysm surface using deep learning approaches. E3S Web of Conferences. 2023 Dec 4;459:02006. doi: 10.1051/e3sconf/202345902006

Author

Borisova, Kseniia ; Fedotova, Yana ; Karpenko, Andrey et al. / Localized growth distribution on the abdominal aortic aneurysm surface using deep learning approaches. In: E3S Web of Conferences. 2023 ; Vol. 459.

BibTeX

@article{5ee74386f6a04c61bbd0b8c208f8233a,
title = "Localized growth distribution on the abdominal aortic aneurysm surface using deep learning approaches",
abstract = "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.",
author = "Kseniia Borisova and Yana Fedotova and Andrey Karpenko and Rustam Mullyadzhanov",
note = "The work is supported by the Russian Science Foundation grant No. 21-15-00091 .; XXXIX Сибирский теплофизический семинар ; Conference date: 28-08-2023 Through 31-08-2023",
year = "2023",
month = dec,
day = "4",
doi = "10.1051/e3sconf/202345902006",
language = "English",
volume = "459",
journal = "E3S Web of Conferences",
issn = "2555-0403",
publisher = "EDP Sciences",

}

RIS

TY - JOUR

T1 - Localized growth distribution on the abdominal aortic aneurysm surface using deep learning approaches

AU - Borisova, Kseniia

AU - Fedotova, Yana

AU - Karpenko, Andrey

AU - Mullyadzhanov, Rustam

N1 - Conference code: XXXIX

PY - 2023/12/4

Y1 - 2023/12/4

N2 - 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.

AB - 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.

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85182749778&origin=inward&txGid=b61ed1b8dc96c7cb94c5578b99c91251

UR - https://www.mendeley.com/catalogue/39a835af-60f8-36e1-aa1b-056ba4cd600d/

U2 - 10.1051/e3sconf/202345902006

DO - 10.1051/e3sconf/202345902006

M3 - Conference article

VL - 459

JO - E3S Web of Conferences

JF - E3S Web of Conferences

SN - 2555-0403

M1 - 02006

T2 - XXXIX Сибирский теплофизический семинар

Y2 - 28 August 2023 through 31 August 2023

ER -

ID: 59578346