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Ridge Detection and Analysis of Susceptibility-Weighted Magnetic Resonance Imaging in Neonatal Hypoxic-Ischaemic Encephalopathy

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Medical Image Understanding and Analysis (MIUA 2020)

Abstract

The purpose of this study is to develop a new automated system to classify susceptibility weighted images (SWI) obtained to evaluate neonatal hypoxic-ischaemic injury, by detecting and analyzing ridges within these images. SW images can depict abnormal cerebral venous contrast as a consequence of abnormal blood flow, perfusion and thus oxygenation in babies with HIE. In this research, a dataset of SWI-MRI images, acquired from 42 infants with HIE during the neonatal period, features are obtained based on ridge analysis of SW images including the width of blood vessels, the change in intensity of the veins’ pixels in comparison with neighboring pixels, the length of blood vessels and Hessian eigenvalues for ridges are extracted. Normalized histogram parameters in the single or combined features are used to classify SWIs by \( kNN \) and random forest classifiers. The mean and standard deviation of the classification accuracies are derived by randomly selecting 11 datasets ten times from those with normal neurological outcome (n = 31) at age 24 months and those with abnormal neurological outcome (n = 11), to avoids classification biases due to any imbalanced data. The feature vectors containing width, intensity, length and eigenvalue show a promising classification accuracy of 78.67% \( \pm \) 2.58%. The features derived from the ridges of the blood vessels have a good discriminative power for prediction of neurological outcome in infants with neonatal HIE. We also employ Support Vector Regression (SVR) to predict the scores of motor and cognitive outcomes assessed 24 months after the birth. Our mean relative errors for cognitive and motor outcome scores are 0.113 \( \pm \) 0.13 and 0.109 \( \pm \) 0.067 respectively.

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References

  1. Friberg, H., Cronberg, T.: Hypoxic–ischemic encephalopathy. In: Seminars in Neurology, vol. 37(01), pp. 003–004. Thieme Medical Publishers, February 2017

    Google Scholar 

  2. James, A., Patel, V.: Hypoxic ischaemic encephalopathy. Paediatr. Child Health 24(9), 385–389 (2014)

    Article  Google Scholar 

  3. Vannucci, R.C.: Hypoxic-ischemic encephalopathy. Am. J. Perinatol. 17(03), 113–120 (2000)

    Article  Google Scholar 

  4. Lally, P.J., et al.: Magnetic resonance spectroscopy assessment of brain injury after moderate hypothermia in neonatal encephalopathy: a prospective multicentre cohort study. Lancet Neurol. 18(1), 35–45 (2019)

    Article  Google Scholar 

  5. Bosemani, T., Poretti, A., Huisman, T.A.: Susceptibility-weighted imaging in pediatric neuroimaging. J. Magn. Reson. Imaging 40(3), 530–544 (2014)

    Article  Google Scholar 

  6. Liauw, L., Van der Grond, J., Van den Berg-Huysmans, A.A., Palm-Meinders, I.H., van Buchem, M.A., van Wezel-Meijler, G.: Hypoxic-ischemic encephalopathy: diagnostic value of conventional MR imaging pulse sequences in term-born neonates. Radiology 247(1), 204–212 (2008)

    Article  Google Scholar 

  7. Obenaus, A., Ashwal, S.: Magnetic resonance imaging in cerebral ischemia: focus on neonates. Neuropharmacology 55(3), 271–280 (2008)

    Article  Google Scholar 

  8. Ghosh, N., Sun, Y., Bhanu, B., Ashwal, S., Obenaus, A.: Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images. Med. Image Anal. 18(7), 1059–1069 (2014)

    Article  Google Scholar 

  9. Murphy, K., et al.: Automatic quantification of ischemic injury on diffusion-weighted MRI of neonatal hypoxic ischemic encephalopathy. NeuroImage Clin. 14, 222–232 (2017)

    Article  Google Scholar 

  10. Weiss, R.J., et al.: Mining multi-site clinical data to develop machine learning MRI biomarkers: application to neonatal hypoxic ischemic encephalopathy. J. Transl. Med. 17(1), 1–16 (2019)

    Article  MathSciNet  Google Scholar 

  11. Wu, S., Mahmoodi, S., Darekar, A., Vollmer, B., Lewis, E., Liljeroth, M.: Feature extraction and classification to diagnose hypoxic-ischemic encephalopathy patients by using susceptibility-weighted MRI images. In: Valdés Hernández, M., González-Castro, V. (eds.) MIUA 2017. CCIS, vol. 723, pp. 527–536. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60964-5_46

    Chapter  Google Scholar 

  12. Barkovich, A.J., Hajnal, B.L., Vigneron, D., Sola, A., Partridge, J.C., Allen, F., Ferriero, D.M.: Prediction of neuromotor outcome in perinatal asphyxia: evaluation of MR scoring systems. Am. J. Neuroradiol. 19(1), 143–149 (1998)

    Google Scholar 

  13. Citraro, L., Mahmoodi, S., Darekar, A., Vollmer, B.: Extended three-dimensional rotation invariant local binary patterns. Image Vis. Comput. 62, 8–18 (2017)

    Article  Google Scholar 

  14. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)

    Article  Google Scholar 

  15. Wu, K., Otoo, E., Shoshani, A.: Optimizing connected component labeling algorithms. In: Medical Imaging 2005: Image Processing, vol. 5747, pp. 1965–1976. International Society for Optics and Photonics, April 2005

    Google Scholar 

  16. Fiorio, C., Gustedt, J.: Two linear time union-find strategies for image processing. Theor. Comput. Sci. 154(2), 165–181 (1996)

    Article  MathSciNet  Google Scholar 

  17. van Schie, P.E., Schijns, J., Becher, J.G., Barkhof, F., van Weissenbruch, M.M., Vermeulen, R.J.: Long-term motor and behavioral outcome after perinatal hypoxic-ischemic encephalopathy. Eur. J. Paediatr. Neurol. 19(3), 354–359 (2015)

    Article  Google Scholar 

  18. Kitamura, G., Kido, D., Wycliffe, N., Jacobson, J.P., Oyoyo, U., Ashwal, S.: Hypoxic-ischemic injury: utility of susceptibility-weighted imaging. Pediatr. Neurol. 45(4), 220–224 (2011)

    Article  Google Scholar 

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Tang, Z., Mahmoodi, S., Dasmahapatra, S., Darekar, A., Vollmer, B. (2020). Ridge Detection and Analysis of Susceptibility-Weighted Magnetic Resonance Imaging in Neonatal Hypoxic-Ischaemic Encephalopathy. In: Papież, B., Namburete, A., Yaqub, M., Noble, J. (eds) Medical Image Understanding and Analysis. MIUA 2020. Communications in Computer and Information Science, vol 1248. Springer, Cham. https://doi.org/10.1007/978-3-030-52791-4_24

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  • DOI: https://doi.org/10.1007/978-3-030-52791-4_24

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