@inproceedings{40318b0b6309499eb13c2023597717f6,
title = "Segmentation of abdominal aortic aneurysm (AAA) based on topology prior model",
abstract = "In this paper, we propose a statistical based method using a topology prior model, integrating both intensity and shape information, to segment abdominal aortic aneurysm (AAA) from computed tomography angiography (CTA) scans. The method was tested on a total of 48 slices taken from 6 different patients and has shown competitive performance compared with the best reported results in the literature. Our method has achieved a mean Dice coefficient of 0.9303±0.0499, and mean Hausdorff distance of 3.5703±3.1941 mm. This method overcomes the major problem faced by currently existing solutions of similar Hounsfield values of neighboring tissues to that of the AAA thrombus. This is a promising medical tool which can be used to analyze the AAA in order to generate an accurate rupture risk indicator.",
keywords = "Abdominal aortic aneurysm, Lumen, Probability, Segmentation, Thrombus, Topology",
author = "Safa Salahat and Ahmed Soliman and Tim McGloughlin and Naoufel Werghi and Ayman El-Baz",
note = "Funding Information: This research was funded with a generous grant from Al-Jalila Foundation, Grant no. AJF201551. Ethics approval from University of Limerick, Ireland was acquired for the used data set in this research. Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017 ; Conference date: 11-07-2017 Through 13-07-2017",
year = "2017",
doi = "10.1007/978-3-319-60964-5\_19",
language = "British English",
isbn = "9783319609638",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "219--228",
editor = "Victor Gonzalez-Castro and \{Valdes Hernandez\}, Maria",
booktitle = "Medical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings",
address = "Germany",
}