Comparison of automatic vessel segmentation techniques for whole body magnetic resonance angiography with limited ground truth data

Andrew McNeil (Lead / Corresponding author), Giulio Degano, Ian Poole, Graeme Houston, Emanuele Trucco

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

1 Citation (Scopus)

Abstract

This work is part of a project aimed at automatically detecting vascular disease in whole body magnetic resonance angiograms (WBMRA). Here we present a comparison of four techniques for automatic artery segmentation in WBMRA data volumes; active contours, two “vesselness” filter approaches (the Frangi filter and Optimally Oriented Flux (OOF)) and a convolutional neural network (Convnet) trained for voxel-wise classification. Their performance was assessed on three manually segmented WBMRA datasets, comparing the maximum Dice Similarity Coefficient (DSC) achieved by each method. Our results show that, in the presence of limited training data, OOF performs best for our three patients, achieving a mean DSC of 0.71 across all patients. By comparison, the 3D Convnet achieved a mean DSC of 0.63. We discuss the potential reasons for these differences, and the implications it has for the automated segmentation of arteries in large WBMRA datasets, where ground truth data is often limited and there are currently no pre-trained 3D Convnet models available, requiring models to be trained from scratch. To the best of our knowledge this is the first comparison of these automated vessel segmentation techniques for WBMRA data, and the first quantitative results of applying a Convnet to vessel segmentation in WBMRA, for which no public sets of manually annotated vascular networks currently exist.

Original languageEnglish
Title of host publicationMedical Image Understanding and Analysis
Subtitle of host publication21st Annual Conference, MIUA 2017, Edinburgh, UK, July 11–13, 2017, Proceedings
EditorsMaria Valdez Hernandez, Victor Gonzalez-Castro
Place of PublicationSwitzerland
PublisherSpringer
Pages144-155
Number of pages12
ISBN (Electronic)9783319609645
ISBN (Print)9783319609638
DOIs
Publication statusPublished - 2017
Event21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017 - Edinburgh, United Kingdom
Duration: 11 Jul 201713 Jul 2017

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer
Volume723
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017
CountryUnited Kingdom
CityEdinburgh
Period11/07/1713/07/17

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