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

^*Corresponding author for this work

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

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.

Original language	English
Title of host publication	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
Subtitle of host publication	Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition
Editors	Antonio Pepe, Gian Marco Melito, Jan Egger
Pages	110-126
Number of pages	17
Volume	1
ISBN (Electronic)	978-3-031-53241-2
DOIs	https://doi.org/10.1007/978-3-031-53241-2_9
Publication status	Published - 14 Feb 2024

Publication series

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

Keywords

Aortic segmentation
Multicenter dataset
Sensitivity analysis

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

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

Cite this

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 (Eds.), 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 (Vol. 1, pp. 110-126). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 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. ed. / Antonio Pepe; Gian Marco Melito; Jan Egger. Vol. 1 2024. p. 110-126 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14539 LNCS).

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

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 (eds), 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. vol. 1, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14539 LNCS, pp. 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, editors, 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. Vol. 1. 2024. p. 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. editor / Antonio Pepe ; Gian Marco Melito ; Jan Egger. Vol. 1 2024. pp. 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

Publication series

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Aortic Dissection

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

ECCOMAS CONGRESS 2024

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

Cite this

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

Abstract

Publication series

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Projects

Aortic Dissection

Activities

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

ECCOMAS CONGRESS 2024

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

Cite this