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VTrails: Inferring vessels with geodesic connectivity trees

Moriconi, S; Zuluaga, MA; Jäger, HR; Nachev, P; Ourselin, S; Cardoso, MJ; (2017) VTrails: Inferring vessels with geodesic connectivity trees. In: Niethammer, M and Styner, M and Aylward, S and Zhu, H and Oguz, I and Yap, PT and Shen, D, (eds.) Information Processing in Medical Imaging. (pp. pp. 672-684). Springer: Cham, Switzerland. Green open access

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Abstract

The analysis of vessel morphology and connectivity has an impact on a number of cardiovascular and neurovascular applications by providing patient-specific high-level quantitative features such as spatial location, direction and scale. In this paper we present an end-to-end approach to extract an acyclic vascular tree from angiographic data by solving a connectivity-enforcing anisotropic fast marching over a voxel-wise tensor field representing the orientation of the underlying vascular tree. The method is validated using synthetic and real vascular images. We compare VTrails against classical and state-of-the-art ridge detectors for tubular structures by assessing the connectedness of the vesselness map and inspecting the synthesized tensor field as proof of concept. VTrails performance is evaluated on images with different levels of degradation: we verify that the extracted vascular network is an acyclic graph (i.e. a tree), and we report the extraction accuracy, precision and recall.

Type: Proceedings paper
Title: VTrails: Inferring vessels with geodesic connectivity trees
Event: 25th International Conference, IPMI 2017, 25-30 June 2017, Boone, NC, USA
ISBN-13: 9783319590493
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-59050-9_53
Publisher version: https://doi.org/10.1007/978-3-319-59050-9_53
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Vascular Network, Tensor Field, Vascular Image, Symmetric Positive Definite, Geodesic Length
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10061186
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