Automatically hemodynamic analysis of AAA from CT images based on deep learning and CFD approaches

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Automatically hemodynamic analysis of AAA from CT images based on deep learning and CFD approaches. / Fedotova, Y. V.; Epifanov, R. U.I.; Karpenko, A. A. et al.

In: Journal of Physics: Conference Series, Vol. 2119, No. 1, 012069, 15.12.2021.

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

BibTeX

@article{c9b28bfdd67d43239b191293d0b8d42b,

title = "Automatically hemodynamic analysis of AAA from CT images based on deep learning and CFD approaches",

abstract = "Abdominal aortic aneurysm is a serious disease which course is accompanied by the development of health complications and often leads to patient death due to aortic rupture. One of the powerful methods to estimate the risk of rupture is three-dimensional patient-specific hemodynamic analysis. In this study, we develop a software tool based on deep learning and CFD methods to perform automated computational hemodynamics with patient-specific geometry reconstructed from computed tomography images.",

author = "Fedotova, {Y. V.} and Epifanov, {R. U.I.} and Karpenko, {A. A.} and Mullyadzhanov, {R. I.}",

note = "Funding Information: Acknowledgements. We thank N. Nikitin, I. Popova, L. Kurdyukov and E. Amelina for helping with the CT image preprocessing and D. Morozov for useful comments on the paper. The work is supported by the Russian Science Foundation grant No. 21-15-00091. Publisher Copyright: {\textcopyright} 2021 Institute of Physics Publishing. All rights reserved.; 37th Siberian Thermophysical Seminar, STS 2021 ; Conference date: 14-09-2021 Through 16-09-2021",

year = "2021",

month = dec,

day = "15",

doi = "10.1088/1742-6596/2119/1/012069",

language = "English",

volume = "2119",

journal = "Journal of Physics: Conference Series",

issn = "1742-6588",

publisher = "IOP Publishing Ltd.",

number = "1",

}

RIS

TY - JOUR

T1 - Automatically hemodynamic analysis of AAA from CT images based on deep learning and CFD approaches

AU - Fedotova, Y. V.

AU - Epifanov, R. U.I.

AU - Karpenko, A. A.

AU - Mullyadzhanov, R. I.

N1 - Funding Information: Acknowledgements. We thank N. Nikitin, I. Popova, L. Kurdyukov and E. Amelina for helping with the CT image preprocessing and D. Morozov for useful comments on the paper. The work is supported by the Russian Science Foundation grant No. 21-15-00091. Publisher Copyright: © 2021 Institute of Physics Publishing. All rights reserved.

PY - 2021/12/15

Y1 - 2021/12/15

N2 - Abdominal aortic aneurysm is a serious disease which course is accompanied by the development of health complications and often leads to patient death due to aortic rupture. One of the powerful methods to estimate the risk of rupture is three-dimensional patient-specific hemodynamic analysis. In this study, we develop a software tool based on deep learning and CFD methods to perform automated computational hemodynamics with patient-specific geometry reconstructed from computed tomography images.

AB - Abdominal aortic aneurysm is a serious disease which course is accompanied by the development of health complications and often leads to patient death due to aortic rupture. One of the powerful methods to estimate the risk of rupture is three-dimensional patient-specific hemodynamic analysis. In this study, we develop a software tool based on deep learning and CFD methods to perform automated computational hemodynamics with patient-specific geometry reconstructed from computed tomography images.

UR - http://www.scopus.com/inward/record.url?scp=85123591237&partnerID=8YFLogxK

U2 - 10.1088/1742-6596/2119/1/012069

DO - 10.1088/1742-6596/2119/1/012069

M3 - Conference article

AN - SCOPUS:85123591237

VL - 2119

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

SN - 1742-6588

IS - 1

M1 - 012069

T2 - 37th Siberian Thermophysical Seminar, STS 2021

Y2 - 14 September 2021 through 16 September 2021

ER -

ID: 35376596