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Comparison of Automatic Vessel Segmentation Techniques for Whole Body Magnetic Resonance Angiography with Limited Ground Truth Data

  • Andrew McNeilEmail author
  • Giulio Degano
  • Ian Poole
  • Graeme Houston
  • Emanuele Trucco
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 723)

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.

References

  1. 1.
    Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)CrossRefzbMATHGoogle Scholar
  2. 2.
    de Brbisson, A., Montana, G.: Deep neural networks for anatomical brain segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 20–28, June 2015Google Scholar
  3. 3.
    Dehkordi, M.T., Sadri, S., Doosthoseini, A.: A review of coronary vessel segmentation algorithms. J. Med. Sig. Sens. 1(1), 49 (2011)Google Scholar
  4. 4.
    Fedorov, A., Beichel, R., Kalpathy-Cramer, J., Finet, J., Fillion-Robin, J.-C., Pujol, S., Bauer, C., Jennings, D., Fennessy, F., Sonka, M., Buatti, J., Aylward, S., Miller, J., Pieper, S., Kikinis, R.: 3D slicer as an image computing platform for the quantitative imaging network. Magn. Reson. Imaging 30(9), 1323–1341 (2012)CrossRefGoogle Scholar
  5. 5.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A., Delp, S. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998). doi: 10.1007/BFb0056195 Google Scholar
  6. 6.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http://www.deeplearningbook.org
  7. 7.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)CrossRefzbMATHGoogle Scholar
  8. 8.
    Kirbas, C., Quek, F.: A review of vessel extraction techniques and algorithms. ACM Comput. Surv. 36(2), 81–121 (2004)CrossRefGoogle Scholar
  9. 9.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105. Curran Associates Inc. (2012)Google Scholar
  10. 10.
    Law, M.W.K., Chung, A.C.S.: Three dimensional curvilinear structure detection using optimally oriented flux. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5305, pp. 368–382. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-88693-8_27 CrossRefGoogle Scholar
  11. 11.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  12. 12.
    Lesage, D., Angelini, E.D., Bloch, I., Funka-Lea, G.: A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes. Med. Image Anal. 13(6), 819–845 (2009)CrossRefGoogle Scholar
  13. 13.
    Lupaşcu, C.A., Tegolo, D., Trucco, E.: Accurate estimation of retinal vessel width using bagged decision trees and an extended multiresolution hermite model. Med. Image Anal. 17(8), 1164–1180 (2013)CrossRefGoogle Scholar
  14. 14.
    McRobbie, D.W., Moore, E.A., Graves, M.J., Prince, M.R.: MRI from Picture to Proton, 2nd edn. Cambridge University Press, Cambridge (2007)Google Scholar
  15. 15.
    Moeskops, P., Viergever, M.A., Mendrik, A.M., de Vries, L.S., Benders, M.J.N.L., Igum, I.: Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans. Med. Imaging 35(5), 1252–1261 (2016)CrossRefGoogle Scholar
  16. 16.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)CrossRefGoogle Scholar
  17. 17.
    Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35(5), 1240–1251 (2016)CrossRefGoogle Scholar
  18. 18.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation, pp. 234–241. Springer International Publishing, Cham (2015)Google Scholar
  19. 19.
    Ruehm, S.G., Goehde, S.C., Goyen, M.: Whole body MR angiography screening. Int. J. Cardiovasc. Imaging 20(6), 587–591 (2004)CrossRefGoogle Scholar
  20. 20.
    Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017)CrossRefGoogle Scholar
  21. 21.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556 (2014)Google Scholar
  22. 22.
    Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetzbMATHGoogle Scholar
  23. 23.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9, June 2015Google Scholar
  24. 24.
    Taha, A.A., Hanbury, A.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15(1), 29 (2015)CrossRefGoogle Scholar
  25. 25.
    Zhang, Y., Matuszewski, B.J., Shark, L.K., Moore, C.J.: Medical image segmentation using new hybrid level-set method. In: 2008 5th International Conference BioMedical Visualization: Information Visualization in Medical and Biomedical Informatics, pp. 71–76, July 2008Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Andrew McNeil
    • 1
    Email author
  • Giulio Degano
    • 1
  • Ian Poole
    • 2
  • Graeme Houston
    • 3
  • Emanuele Trucco
    • 1
  1. 1.CVIP, Computing, School of Science and EngineeringUniversity of DundeeDundeeUK
  2. 2.Toshiba Medical Visualization Systems EuropeEdinburghUK
  3. 3.School of MedicineNinewells Hospital and Medical SchoolDundeeUK

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