Deep learning and particle filter-based aortic dissection vessel tree segmentation

Yuan Jin; Antonio Pepe; Jianning Li; Christina Gsaxner; Jan Egger

doi:10.1117/12.2588220

15 February 2021 Deep learning and particle filter-based aortic dissection vessel tree segmentation

Yuan Jin, Antonio Pepe, Jianning Li, Christina Gsaxner, Jan Egger

Proceedings Volume 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging; 116001W (2021) https://doi.org/10.1117/12.2588220
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

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.

Conference Presentation

Citation Download Citation

Yuan Jin, Antonio Pepe, Jianning Li, Christina Gsaxner, and Jan Egger "Deep learning and particle filter-based aortic dissection vessel tree segmentation", Proc. SPIE 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging, 116001W (15 February 2021); https://doi.org/10.1117/12.2588220

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available