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 language | English |
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Title of host publication | Medical Image Understanding and Analysis |
Subtitle of host publication | 21st Annual Conference, MIUA 2017, Edinburgh, UK, July 11–13, 2017, Proceedings |
Editors | Maria Valdez Hernandez, Victor Gonzalez-Castro |
Place of Publication | Switzerland |
Publisher | Springer |
Pages | 144-155 |
Number of pages | 12 |
ISBN (Electronic) | 9783319609645 |
ISBN (Print) | 9783319609638 |
DOIs | |
Publication status | Published - 2017 |
Event | 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017 - Edinburgh, United Kingdom Duration: 11 Jul 2017 → 13 Jul 2017 |
Publication series
Name | Communications in Computer and Information Science |
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Publisher | Springer |
Volume | 723 |
ISSN (Print) | 1865-0929 |
ISSN (Electronic) | 1865-0937 |
Conference
Conference | 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017 |
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Country/Territory | United Kingdom |
City | Edinburgh |
Period | 11/07/17 → 13/07/17 |
ASJC Scopus subject areas
- General Computer Science
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Computer-Assisted Analysis of Arterial Narrowing in Whole-Body Magnetic Resonance Angiography
McNeil, A. (Author), Trucco, M. (Supervisor), Houston, G. (Supervisor) & Poole, I. (Supervisor), 2020Student thesis: Doctoral Thesis › Doctor of Philosophy
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