Deep Learning Meets Computational Fluid Dynamics to Assess CAD in CCTA

Filip Malawski; Jarosław Gośliński; Mikołaj Stryja; Katarzyna Jesionek; Marcin Kostur; Karol Miszalski-Jamka; Jakub Nalepa

doi:10.1007/978-3-031-17721-7_2

Deep Learning Meets Computational Fluid Dynamics to Assess CAD in CCTA

Filip Malawski
, Jarosław Gośliński
, Mikołaj Stryja
, Katarzyna Jesionek
, Marcin Kostur
, Karol Miszalski-Jamka
, Jakub Nalepa

Department of Algorithmics and Software (RAU/RAU5)

Graylight Imaging
AGH University of Krakow
University of Silesia in Katowice

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

Early diagnosis and effective monitoring of the coronary artery disease are critical in ensuring its effective treatment. Although there are established invasive examinations to assess this condition, the current research focus is put on non-invasive procedures. Here, the coronary computed tomography angiography is the first-choice modality, but its manual analysis is cost-inefficient, lacks reproducibility, and suffers from significant inter- and intra-rater disagreement. We tackle those issues and introduce an end-to-end deep learning-powered pipeline for automated analysis of such imagery which additionally exploits computational fluid dynamics to capture the functional vessel characteristics. Our experiments, performed over clinically acquired scans, revealed that the suggested segmentation approaches not only outperform state-of-the-art nnU-Nets, but also lead to the blood-flow parameters which are in strong agreement with those elaborated for the ground-truth delineations.

Original language	English
Title of host publication	Applications of Medical Artificial Intelligence - 1st International Workshop, AMAI 2022, Held in Conjunction with MICCAI 2022, Proceedings
Editors	Shandong Wu, Behrouz Shabestari, Lei Xing
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	8-17
Number of pages	10
ISBN (Print)	9783031177200
DOIs	https://doi.org/10.1007/978-3-031-17721-7_2
Publication status	Published - 2022
Event	1st International Workshop on Applications of Medical Artificial Intelligence, AMAI 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022 - Virtual, Online Duration: 18 Sept 2022 → 18 Sept 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13540 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st International Workshop on Applications of Medical Artificial Intelligence, AMAI 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
City	Virtual, Online
Period	18/09/22 → 18/09/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Blood flow simulation
CCTA
Coronary arteries
Coronary artery disease
Segmentation
U-Net

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-17721-7_2

Cite this

Malawski, F., Gośliński, J., Stryja, M., Jesionek, K., Kostur, M., Miszalski-Jamka, K., & Nalepa, J. (2022). Deep Learning Meets Computational Fluid Dynamics to Assess CAD in CCTA. In S. Wu, B. Shabestari, & L. Xing (Eds.), Applications of Medical Artificial Intelligence - 1st International Workshop, AMAI 2022, Held in Conjunction with MICCAI 2022, Proceedings (pp. 8-17). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13540 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-17721-7_2

Malawski, Filip ; Gośliński, Jarosław ; Stryja, Mikołaj et al. / Deep Learning Meets Computational Fluid Dynamics to Assess CAD in CCTA. Applications of Medical Artificial Intelligence - 1st International Workshop, AMAI 2022, Held in Conjunction with MICCAI 2022, Proceedings. editor / Shandong Wu ; Behrouz Shabestari ; Lei Xing. Springer Science and Business Media Deutschland GmbH, 2022. pp. 8-17 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Deep Learning Meets Computational Fluid Dynamics to Assess CAD in CCTA",

abstract = "Early diagnosis and effective monitoring of the coronary artery disease are critical in ensuring its effective treatment. Although there are established invasive examinations to assess this condition, the current research focus is put on non-invasive procedures. Here, the coronary computed tomography angiography is the first-choice modality, but its manual analysis is cost-inefficient, lacks reproducibility, and suffers from significant inter- and intra-rater disagreement. We tackle those issues and introduce an end-to-end deep learning-powered pipeline for automated analysis of such imagery which additionally exploits computational fluid dynamics to capture the functional vessel characteristics. Our experiments, performed over clinically acquired scans, revealed that the suggested segmentation approaches not only outperform state-of-the-art nnU-Nets, but also lead to the blood-flow parameters which are in strong agreement with those elaborated for the ground-truth delineations.",

keywords = "Blood flow simulation, CCTA, Coronary arteries, Coronary artery disease, Segmentation, U-Net",

author = "Filip Malawski and Jaros{\l}aw Go{\'s}li{\'n}ski and Miko{\l}aj Stryja and Katarzyna Jesionek and Marcin Kostur and Karol Miszalski-Jamka and Jakub Nalepa",

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Malawski, F, Gośliński, J, Stryja, M, Jesionek, K, Kostur, M, Miszalski-Jamka, K & Nalepa, J 2022, Deep Learning Meets Computational Fluid Dynamics to Assess CAD in CCTA. in S Wu, B Shabestari & L Xing (eds), Applications of Medical Artificial Intelligence - 1st International Workshop, AMAI 2022, Held in Conjunction with MICCAI 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13540 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 8-17, 1st International Workshop on Applications of Medical Artificial Intelligence, AMAI 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022, Virtual, Online, 18/09/22. https://doi.org/10.1007/978-3-031-17721-7_2

Deep Learning Meets Computational Fluid Dynamics to Assess CAD in CCTA. / Malawski, Filip; Gośliński, Jarosław; Stryja, Mikołaj et al.
Applications of Medical Artificial Intelligence - 1st International Workshop, AMAI 2022, Held in Conjunction with MICCAI 2022, Proceedings. ed. / Shandong Wu; Behrouz Shabestari; Lei Xing. Springer Science and Business Media Deutschland GmbH, 2022. p. 8-17 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13540 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Deep Learning Meets Computational Fluid Dynamics to Assess CAD in CCTA

AU - Malawski, Filip

AU - Gośliński, Jarosław

AU - Stryja, Mikołaj

AU - Jesionek, Katarzyna

AU - Kostur, Marcin

AU - Miszalski-Jamka, Karol

AU - Nalepa, Jakub

PY - 2022

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N2 - Early diagnosis and effective monitoring of the coronary artery disease are critical in ensuring its effective treatment. Although there are established invasive examinations to assess this condition, the current research focus is put on non-invasive procedures. Here, the coronary computed tomography angiography is the first-choice modality, but its manual analysis is cost-inefficient, lacks reproducibility, and suffers from significant inter- and intra-rater disagreement. We tackle those issues and introduce an end-to-end deep learning-powered pipeline for automated analysis of such imagery which additionally exploits computational fluid dynamics to capture the functional vessel characteristics. Our experiments, performed over clinically acquired scans, revealed that the suggested segmentation approaches not only outperform state-of-the-art nnU-Nets, but also lead to the blood-flow parameters which are in strong agreement with those elaborated for the ground-truth delineations.

AB - Early diagnosis and effective monitoring of the coronary artery disease are critical in ensuring its effective treatment. Although there are established invasive examinations to assess this condition, the current research focus is put on non-invasive procedures. Here, the coronary computed tomography angiography is the first-choice modality, but its manual analysis is cost-inefficient, lacks reproducibility, and suffers from significant inter- and intra-rater disagreement. We tackle those issues and introduce an end-to-end deep learning-powered pipeline for automated analysis of such imagery which additionally exploits computational fluid dynamics to capture the functional vessel characteristics. Our experiments, performed over clinically acquired scans, revealed that the suggested segmentation approaches not only outperform state-of-the-art nnU-Nets, but also lead to the blood-flow parameters which are in strong agreement with those elaborated for the ground-truth delineations.

KW - Blood flow simulation

KW - CCTA

KW - Coronary arteries

KW - Coronary artery disease

KW - Segmentation

KW - U-Net

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U2 - 10.1007/978-3-031-17721-7_2

DO - 10.1007/978-3-031-17721-7_2

M3 - Conference contribution

AN - SCOPUS:85140483087

SN - 9783031177200

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 8

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BT - Applications of Medical Artificial Intelligence - 1st International Workshop, AMAI 2022, Held in Conjunction with MICCAI 2022, Proceedings

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PB - Springer Science and Business Media Deutschland GmbH

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ER -

Malawski F, Gośliński J, Stryja M, Jesionek K, Kostur M, Miszalski-Jamka K et al. Deep Learning Meets Computational Fluid Dynamics to Assess CAD in CCTA. In Wu S, Shabestari B, Xing L, editors, Applications of Medical Artificial Intelligence - 1st International Workshop, AMAI 2022, Held in Conjunction with MICCAI 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 8-17. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-17721-7_2