Combining images and anatomical knowledge to improve automated vein segmentation in MRI

Phillip G. D. Ward, Nicholas J. Ferris, Parnesh Raniga, David L. Dowe, Amanda C. L. Ng, David G. Barnes, Gary F. Egan

Research output: Contribution to journalArticleResearchpeer-review

21 Citations (Scopus)


Purpose: To improve the accuracy of automated vein segmentation by combining susceptibility-weighted images (SWI), quantitative susceptibility maps (QSM), and a vein atlas to produce a resultant image called a composite vein image (CV image). Method: An atlas was constructed in common space from manually traced MRI images from ten volunteers. The composite vein image was derived for each subject as a weighted sum of three inputs; an SWI image, a QSM image and the vein atlas. The weights for each input and each anatomical location, called template priors, were derived by assessing the accuracy of each input over an independent data set. The accuracy of vein segmentations derived automatically from each of the CV image, SWI, and QSM image sets was assessed by comparison with manual tracings. Three different automated vein segmentation techniques were used, and ten performance metrics evaluated.Results: Vein segmentations using the CV image were comprehensively better than those derived from SWI or QSM images (mean Cohen's d ¼ 1.1). Sixty permutations of performance metric, benchmark image, and automated segmentation technique were evaluated. Vein identification improvements that were both large and significant (Cohen's d > 0.80, p < 0.05) were found in 77% of the permutations, compared to no improvement in 5%.Conclusion: The accuracy of automated vein segmentations derived from the composite vein image was overwhelmingly superior to segmentations derived from SWI or QSM alone. 
Original languageEnglish
Pages (from-to)294-605
Number of pages12
Publication statusPublished - 2018


  • MRI
  • QSM
  • SWI
  • Vein atlas
  • Vein segmentation
  • Cerebral vasculature

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