Advertisement

Automatic Quality Control of Cardiac MRI Segmentation in Large-Scale Population Imaging

  • Robert Robinson
  • Vanya V. Valindria
  • Wenjia Bai
  • Hideaki Suzuki
  • Paul M. Matthews
  • Chris Page
  • Daniel Rueckert
  • Ben Glocker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)

Abstract

The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools such as image segmentation methods are employed to derive quantitative measures or biomarkers for further analyses. Manual inspection and visual QC of each segmentation result is not feasible at large scale. However, it is important to be able to detect when an automatic method fails to avoid inclusion of wrong measurements into subsequent analyses which could otherwise lead to incorrect conclusions. To overcome this challenge, we explore an approach for predicting segmentation quality based on reverse classification accuracy, which enables us to discriminate between successful and failed cases. We validate this approach on a large cohort of cardiac MRI for which manual QC scores were available. Our results on 7,425 cases demonstrate the potential for fully automatic QC in the context of large-scale population imaging such as the UK Biobank Imaging Study.

Notes

Acknowledgements

RR is funded by GSK and EPSRC CDT in Medical Imaging (EP/L015226/1); VV by Indonesia Endowment for Education (LPDP) Indonesian Presidential PhD Scholarship; HS by Research Fellowship from Uehara Memorial Foundation; PMM acknowledges support of Imperial Healthcare Trust BRC, EPSRC Centre for Mathematics in Precision Healthcare and MRC.

References

  1. 1.
    Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., Liu, B., Matthews, P., Ong, G., Pell, J., Silman, A., Young, A., Sprosen, T., Peakman, T., Collins, R.: UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12(3), 1–10 (2015)CrossRefGoogle Scholar
  2. 2.
    Shariff, A., Kangas, J., Coelho, L.P., Quinn, S., Murphy, R.F.: Automated image analysis for high-content screening and analysis. J. Biomol. Screen. 15(7), 726–734 (2010)Google Scholar
  3. 3.
    de Bruijne, M.: Machine learning approaches in medical image analysis: from detection to diagnosis. Med. Image Anal. 33, 94–7 (2016)Google Scholar
  4. 4.
    Crum, W.R., Camara, O., Hill, D.L.G.: Generalized overlap measures for evaluation and validation in medical image analysis. IEEE Trans. Med. Imag. 25(11), 1451–1461 (2006)CrossRefGoogle Scholar
  5. 5.
    Taha, A.A., Hanbury, A.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med. Imag. 15, 29 (2015)CrossRefGoogle Scholar
  6. 6.
    Valindria, V.V., Lavdas, I., Bai, W., Kamnitsas, K., Aboagye, E.O., Rockall, A.G., Rueckert, D., Glocker, B.: Reverse classification accuracy: predicting segmentation performance in the absence of ground truth. IEEE Trans. Med. Imag.PP, 1–1 (2017)Google Scholar
  7. 7.
    Zhong, E., Fan, W., Yang, Q., Verscheure, O., Ren, J.: Cross validation framework to choose amongst models and datasets for transfer learning. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS (LNAI), vol. 6323, pp. 547–562. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-15939-8_35 CrossRefGoogle Scholar
  8. 8.
    Fan, W., Davidson, I.: Reverse testing. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2006, p. 147. ACM Press, New York (2006)Google Scholar
  9. 9.
    Kohlberger, T., Singh, V., Alvino, C., Bahlmann, C., Grady, L.: Evaluating segmentation error without ground truth. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7510, pp. 528–536. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-33415-3_65 CrossRefGoogle Scholar
  10. 10.
    Carapella, V., Jiménez-Ruiz, E., Lukaschuk, E., Aung, N., Fung, K., Paiva, J., Sanghvi, M., Neubauer, S., Petersen, S., Horrocks, I., Piechnik, S.: Towards the semantic enrichment of free-text annotation of image quality assessment for UK biobank cardiac cine MRI scans. In: Carneiro, G., et al. (eds.) LABELS/DLMIA -2016. LNCS, vol. 10008, pp. 238–248. Springer, Cham (2016). doi: 10.1007/978-3-319-46976-8_25 Google Scholar
  11. 11.
    Zhang, L., Gooya, A., Dong, B., Hua, R., Petersen, S.E., Medrano-Gracia, P., Frangi, A.F.: Automated quality assessment of cardiac MR images using convolutional neural networks. In: Tsaftaris, S.A., Gooya, A., Frangi, A.F., Prince, J.L. (eds.) SASHIMI 2016. LNCS, vol. 9968, pp. 138–145. Springer, Cham (2016). doi: 10.1007/978-3-319-46630-9_14 CrossRefGoogle Scholar
  12. 12.
    Zikic, D., Glocker, B., Criminisi, A.: Encoding atlases by randomized classification forests for efficient multi-atlas label propagation. Med. Image Anal. 18(8), 1262–1273 (2014)Google Scholar
  13. 13.
    Bai, W., Shi, W., O’Regan, D.P., Tong, T., Wang, H., Jamil-Copley, S., Peters, N.S., Rueckert, D.: A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: application to cardiac MR images. IEEE Trans. Med. Imag. 32(7), 1302–1315 (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Robert Robinson
    • 1
  • Vanya V. Valindria
    • 1
  • Wenjia Bai
    • 1
  • Hideaki Suzuki
    • 2
  • Paul M. Matthews
    • 2
  • Chris Page
    • 3
  • Daniel Rueckert
    • 1
  • Ben Glocker
    • 1
  1. 1.BioMedIA Group, Deptartment of ComputingImperial College LondonLondonUK
  2. 2.Division of Brain Sciences, Department of MedicineImperial College LondonLondonUK
  3. 3.Clinical Innovation and Digital PlatformsGlaxoSmithKline R&DUxbridgeUK

Personalised recommendations