Optimizing Aortic Segmentation with an Innovative Quality Assessment: The Role of Global Sensitivity Analysis

Gian Marco Melito; Antonio Pepe; Alireza Jafarinia; Thomas Krispel; Jan Egger

doi:10.1007/978-3-031-53241-2_9

Optimizing Aortic Segmentation with an Innovative Quality Assessment: The Role of Global Sensitivity Analysis

Gian Marco Melito^*, Antonio Pepe, Alireza Jafarinia, Thomas Krispel, Jan Egger

^*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

Abstract

Precise aortic vessel tree segmentation is critical in the continuously evolving medical imaging domain. This study highlights the role of global sensitivity analysis in stimulating innovation in quality assessment techniques for aortic segmentation. In this methodology paper, we propose a novel method that integrates global sensitivity analysis with data augmentation techniques, aiming to enhance the reliability and robustness of segmentation algorithms. This approach aims to quantify the challenges posed by image variations and aspires to establish a methodology capable of managing a spectrum of image scenarios. The study also explores the implications of achieving accurate segmentations for clinical monitoring and computational fluid dynamics simulations of the aortic vessel tree. The presented approach was used for the final ranking of the MICCAI 2023 SEG.A. challenge to account for image variations in evaluating the submitted algorithms.

Originalsprache	englisch
Titel	Segmentation of the Aorta. Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition - First Challenge, SEG.A. 2023, Held in Conjunction with MICCAI 2023, Proceedings
Untertitel	Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition
Redakteure/-innen	Antonio Pepe, Gian Marco Melito, Jan Egger
Seiten	110-126
Seitenumfang	17
Band	1
ISBN (elektronisch)	978-3-031-53241-2
DOIs	https://doi.org/10.1007/978-3-031-53241-2_9
Publikationsstatus	Veröffentlicht - 14 Feb. 2024

Publikationsreihe

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band	14539 LNCS
ISSN (Print)	0302-9743
ISSN (elektronisch)	1611-3349

ASJC Scopus subject areas

Theoretische Informatik
Allgemeine Computerwissenschaft

Zugriff auf Dokument

10.1007/978-3-031-53241-2_9

Andere Dateien und Links

Verknüpfung zur Publikation in Scopus

Aortendissektion
Egger, J., Pepe, A., Schmalstieg, D., Schussnig, R., von der Linden, W., Melito, G. M., Holzapfel, G., Ramalho Queiroz Pacheco, D., Jafarinia, A., Brenn, G., Ranftl, S., Müller, T. S., Gupta, I., Steinbach, O., Fries, T., Badeli, V., Hochrainer, T., Schanz, M., Rolf-Pissarczyk, M., Biro, O. & Ellermann, K.
1/01/18 → 31/12/20
Projekt: Forschungsprojekt

1 Herausgebertätigkeit
1 Vortrag bei Workshop, Seminar oder Kurs

SEG.A. 2023 - Towards the Automatic Segmentation, Modeling and Meshing of the Aortic Vessel Tree from Multicenter Acquisitions (Veranstaltung)
Antonio Pepe (Herausgeber/in), Gian Marco Melito (Herausgeber/in) & Jan Egger (Herausgeber/in)
11 März 2024
Aktivität: Peer review oder Herausgebertätigkeit › Herausgebertätigkeit
Sensitivity analysis as a tool for optimization of aortic segmentation algorithms from CT scan images
Gian Marco Melito (Redner/in)
7 Mai 2024
Aktivität: Vortrag oder Präsentation › Vortrag bei Workshop, Seminar oder Kurs › Science to science

Dieses zitieren

Melito, G. M., Pepe, A., Jafarinia, A., Krispel, T., & Egger, J. (2024). Optimizing Aortic Segmentation with an Innovative Quality Assessment: The Role of Global Sensitivity Analysis. in A. Pepe, G. M. Melito, & J. Egger (Hrsg.), Segmentation of the Aorta. Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition - First Challenge, SEG.A. 2023, Held in Conjunction with MICCAI 2023, Proceedings: Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition (Band 1, S. 110-126). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 14539 LNCS). https://doi.org/10.1007/978-3-031-53241-2_9

Optimizing Aortic Segmentation with an Innovative Quality Assessment: The Role of Global Sensitivity Analysis. / Melito, Gian Marco; Pepe, Antonio; Jafarinia, Alireza et al.
Segmentation of the Aorta. Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition - First Challenge, SEG.A. 2023, Held in Conjunction with MICCAI 2023, Proceedings: Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition. Hrsg. / Antonio Pepe; Gian Marco Melito; Jan Egger. Band 1 2024. S. 110-126 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 14539 LNCS).

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

Melito, GM, Pepe, A, Jafarinia, A, Krispel, T & Egger, J 2024, Optimizing Aortic Segmentation with an Innovative Quality Assessment: The Role of Global Sensitivity Analysis. in A Pepe, GM Melito & J Egger (Hrsg.), Segmentation of the Aorta. Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition - First Challenge, SEG.A. 2023, Held in Conjunction with MICCAI 2023, Proceedings: Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition. Bd. 1, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 14539 LNCS, S. 110-126. https://doi.org/10.1007/978-3-031-53241-2_9

Melito GM, Pepe A, Jafarinia A, Krispel T , Egger J. Optimizing Aortic Segmentation with an Innovative Quality Assessment: The Role of Global Sensitivity Analysis. in Pepe A, Melito GM, Egger J, Hrsg., Segmentation of the Aorta. Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition - First Challenge, SEG.A. 2023, Held in Conjunction with MICCAI 2023, Proceedings: Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition. Band 1. 2024. S. 110-126. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-53241-2_9

Melito, Gian Marco ; Pepe, Antonio ; Jafarinia, Alireza et al. / Optimizing Aortic Segmentation with an Innovative Quality Assessment: The Role of Global Sensitivity Analysis. Segmentation of the Aorta. Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition - First Challenge, SEG.A. 2023, Held in Conjunction with MICCAI 2023, Proceedings: Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition. Hrsg. / Antonio Pepe ; Gian Marco Melito ; Jan Egger. Band 1 2024. S. 110-126 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Optimizing Aortic Segmentation with an Innovative Quality Assessment: The Role of Global Sensitivity Analysis

Abstract

Publikationsreihe

ASJC Scopus subject areas

Zugriff auf Dokument

Andere Dateien und Links

Fingerprint

Projekte

Aortendissektion

Aktivitäten

SEG.A. 2023 - Towards the Automatic Segmentation, Modeling and Meshing of the Aortic Vessel Tree from Multicenter Acquisitions (Veranstaltung)

Sensitivity analysis as a tool for optimization of aortic segmentation algorithms from CT scan images

Dieses zitieren