Automatic Identification and Localisation of Potential Malignancies in Contrast-Enhanced Ultrasound Liver Scans Using Spatio-Temporal Features

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8676)

Abstract

The identification and localisation of a focal liver lesion (FLL) in Contrast-Enhanced Ultrasound (CEUS) video sequences is crucial for liver cancer diagnosis, treatment planning and follow-up management. Currently, localisation and classification of FLLs between benign and malignant cases in CEUS are routinely performed manually by radiologists, in order to proceed with making a diagnosis, leading to subjective results, prone to misinterpretation and human error. This paper describes a methodology to assist clinicians who regularly perform these tasks, by discharging benign FLL cases and localise potential malignancies in a fully automatic manner by exploiting the perfusion dynamics of a CEUS video. The proposed framework uses local variations of intensity to distinguish between hyper- and hypo-enhancing regions and then analyse their spatial configuration to identify potentially malignant cases. Automatic localisation of the potential malignancy on the image plane is then addressed by clustering, using Expectation-Maximisation for Gaussian Mixture Models. A novel feature that combines description of local dynamic behaviour with spatial proximity is used in this process. Quantitative evaluation, on real clinical data from a retrospective multi-centre study, demonstrates the value of the proposed method.

Keywords

Localisation Malignancy identification Contrast-enhanced ultrasound Focal liver lesion Liver Perfusion Clustering 

References

  1. 1.
    Claudon, M., Dietrich, C.F., Choi, B.I., Cosgrove, D.O., Kudo, M., Nolsøe, C.P., et al.: Guidelines and good clinical practice recommendations for CEUS in the liver - update 2012: a WFUMB-EFSUMB initiative in cooperation with representatives of AFSUMB, AIUM, ASUM, FLAUS and ICUS. Ultrasound Med. Biol. 39, 187–210 (2013)CrossRefGoogle Scholar
  2. 2.
    Harvey, C.J., Blomley, M.J.K., Eckersley, R.J., Cosgrove, D.O.: Developments in ultrasound contrast media. Eur. Radiol. 11, 675–689 (2001)CrossRefGoogle Scholar
  3. 3.
    Strobel, D., Seitz, K., Blank, W., Schuler, A., Dietrich, C.F., von Herbay, A., et al.: Tumor-specific vascularization pattern of liver metastasis, hepatocellular carcinoma, hemangioma and focal nodular hyperplasia in the differential diagnosis of 1349 liver lesions in contrast-enhanced ultrasound (CEUS). Ultraschall Med. 30, 376–382 (2009)CrossRefGoogle Scholar
  4. 4.
    Westwood, M.E., Joore, M.A., Grutters, J.P.C., Redekop, W.K., Armstrong, N., Lee, K., et al.: Contrast-enhanced ultrasound using sonovue\(\textregistered \)(sulphur hexafluoride microbubbles) compared with contrast-enhanced computed tomography and contrast-enhanced magnetic resonance imaging for the characterisation of focal liver lesions and detection of liver metastases: a systematic review and cost-effectiveness analysis. Health Technol. Assess. 17, 1–243 (2013)Google Scholar
  5. 5.
    Wilson, S.R., Burns, P.N.: Microbubble-enhanced US in body imaging: what role? Radiology 257, 24–39 (2010)CrossRefGoogle Scholar
  6. 6.
    Bakas, S., Hunter, G., Thiebaud, C., Makris, D.: Spot the best frame: towards intelligent automated selection of the optimal frame for initialisation of focal liver lesion candidates in contrast-enhanced ultrasound video sequences. In: 9th International Conference on Intelligent Environments, pp. 196–203. IEEE Press (2013)Google Scholar
  7. 7.
    Bakas, S., Chatzimichail, K., Hoppe, A., Galariotis, V., Hunter, G., Makris, D.: Histogram-based motion segmentation and characterisation of focal liver lesions in CEUS. Ann. BMVA 2012, 1–14 (2012)Google Scholar
  8. 8.
    Bakas, S., Hoppe, A., Chatzimichail, K., Galariotis, V., Hunter, G., Makris, D.: Focal liver lesion tracking in CEUS for characterisation based on dynamic behaviour. In: Bebis, G., et al. (eds.) ISVC 2012, Part I. LNCS, vol. 7431, pp. 32–41. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Bakas, S., Sidhu, P.S., Sellars, M.E., Hunter, G.J.A., Makris, D., Chatzimichail, K.: Non-invasive offline characterisation of contrast-enhanced ultrasound evaluations of focal liver lesions: dynamic assessment using a new tracking method. In: 20th European Congress of Radiology (2014)Google Scholar
  10. 10.
    Rognin, N., Campos, R., Thiran, J.P., Messager, T., Broillet, A., Frinking, P., et al.: A new approach for automatic motion compensation for improved estimation of perfusion quantification parameters in ultrasound imaging. In: 8th French Conference on Acoustics, pp. 61–65 (2006)Google Scholar
  11. 11.
    Ta, C.N., Kono, Y., Barback, C.V., Mattrey, R.F., Kummel, A.C.: Automating tumor classification with pixel-by-pixel contrast-enhanced ultrasound perfusion kinetics. J. Vac. Sci. Technol. B Nanotechnol. Microelectron. 30, 02C103 (2012)Google Scholar
  12. 12.
    Shekhar, R., Zagrodsky, V.: Mutual information-based rigid and nonrigid registration of ultrasound volumes. IEEE Trans. Med. Imaging 21, 9–22 (2002)CrossRefGoogle Scholar
  13. 13.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)CrossRefGoogle Scholar
  14. 14.
    Zhou, J., Huang, W., Xiong, W., Chen, W., Venkatesh, S.K., Tian, Q.: Delineation of liver tumors from CT scans using spectral clustering with out-of-sample extension and multi-windowing. In: Yoshida, H., Hawkes, D., Vannier, M.W. (eds.) Abdominal Imaging 2012. LNCS, vol. 7601, pp. 246–254. Springer, Heidelberg (2012)Google Scholar
  15. 15.
    Crum, W.R.: Spectral clustering and label fusion for 3D tissue classification: sensitivity and consistency analysis. Ann. BMVA 2009, 1–12 (2009)Google Scholar
  16. 16.
    Song, Z., Tustison, N., Avants, B., Gee, J.: Adaptive graph cuts with tissue priors for brain MRI segmentation. In: 3rd International Symposium on Biomedical Imaging: Nano to Macro, pp. 762–765. IEEE Press (2006)Google Scholar
  17. 17.
    Ambai, M., Yoshida, Y.: CARD: compact and real-time descriptors. In: IEEE International Conference on Computer Vision, pp. 97–104 (2011)Google Scholar
  18. 18.
    Figueiredo, M.A.T., Jain, A.K.: Unsupervised learning on finite mixture models. IEEE Trans. Pattern Anal. Mach. Intell. 24, 381–396 (2002)CrossRefGoogle Scholar
  19. 19.
    Schneider, M.: Characteristics of sonovue. Echocardiography 16, 743–746 (1999)CrossRefGoogle Scholar
  20. 20.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Digital Imaging Research Centre, Faculty of Science, Engineering and ComputingKingston UniversityLondonUK
  2. 2.Department of RadiologyKing’s College HospitalLondonUK
  3. 3.Evgenidion HospitalNational and Kapodistrian UniversityAthensGreece

Personalised recommendations