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

• Published in: bioRxiv
• Main Authors: Ward, Phillip G D, Ferris, Nicholas J, Raniga, Parnesh, Dowe, David L, Ng, Amanda C L, Barnes, David G, Egan, Gary F
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 20.06.2017
• Subjects: Accuracy; Automation; Business metrics; Image processing; Magnetic resonance imaging; Segmentation
• DOI: 10.1101/152389

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 1072 manually traced 2D-slices. The composite vein image was derived for each subject as a weighted sum of three inputs; a 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 venograms 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 = 1.1). Sixty permutations of performance metric 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 venograms derived from the composite vein image was overwhelmingly superior to venograms derived from SWI or QSM alone.