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VTrails: Inferring vessels with geodesic connectivity trees

Research output: Chapter in Book/Report/Conference proceedingConference paper

Stefano Moriconi, Maria A. Zuluaga, H. Rolf Jäger, Parashkev Nachev, Sébastien Ourselin, M. Jorge Cardoso

Original languageEnglish
Title of host publicationInformation Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings
PublisherSpringer Verlag
Number of pages13
Volume10265 LNCS
ISBN (Print)9783319590493
Publication statusE-pub ahead of print - 23 May 2017
Event25th International Conference on Information Processing in Medical Imaging, IPMI 2017 - Boone, United States
Duration: 25 Jun 201730 Jun 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10265 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference25th International Conference on Information Processing in Medical Imaging, IPMI 2017
CountryUnited States


  • VTrails Inferring Vessels with_MORICONI_Publishedonline23May2017_GREEN AAM

    VTrails_Inferring_Vessels_with_MORICONI_Publishedonline23May2017_GREEN_AAM.pdf, 4.98 MB, application/pdf


    Accepted author manuscript

    © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

  • 1806.03111v1

    1806.03111v1.pdf, 4.98 MB, application/pdf


King's Authors


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.

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