@inproceedings{d3ceb5fffb7b4cd3b2f62e92e61ea288,
title = "Deep learning and particle filter-based aortic dissection vessel tree segmentation",
abstract = "Aortic dissections (AD) are injuries of the inner vessel wall of the (human) aorta. As this disease poses a significant threat to a patient's life, it is crucial to observe and analyze the progression of the dissection over the course of the disease. The clinical examinations are usually performed with the application of Computed Tomography (CT) or Computed Tomography Angiography (CTA), based on which, automated post-processing procedures would be beneficial for the management of critical pathologies. One of the main tasks during post-processing is aorta segmentation. Different methods have been developed for the segmentation of aorta, including the tracking methods, the active contour/surface methods and the deep learning methods. In this study, a method for the automatic segmentation of aorta and its branches from original thorax CT and CTA images is introduced. The aorta is segmented based on deep learning algorithm and afterwards the branches are tracked based on particle filter algorithm. ",
keywords = "Aortic Dissection, Computed Tomography Angiography, Deep Learning, Particle Filter., Segmentation, V-Net",
author = "Yuan Jin and Antonio Pepe and Jianning Li and Christina Gsaxner and Jan Egger",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging ; Conference date: 15-02-2021 Through 19-02-2021",
year = "2021",
doi = "10.1117/12.2588220",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Gimi, {Barjor S.} and Andrzej Krol",
booktitle = "Medical Imaging 2021",
address = "United States",
}
