Advertisement

Mass Univariate Regression Analysis for Three-Dimensional Liver Image-Derived Phenotypes

Conference paper
  • 169 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12722)

Abstract

Image-derived phenotypes of abdominal organs from magnetic resonance imaging reveal variations in volume and shape and may be used to model changes in a normal versus pathological organ and improve diagnosis. Computational atlases of anatomical organs provide many advantages in quantifying and modeling differences in shape and size of organs for population imaging studies. Here we made use of liver segmentations derived from Dixon MRI for 2,730 UK Biobank participants to create 3D liver meshes. We computed the signed distances between a reference and subject-specific meshes to define the surface-to-surface (S2S) phenotype. We employed mass univariate regression analysis to compare the S2S values from the liver meshes to image-derived phenotypes specific to the liver, such as proton density fat fraction and iron concentration while adjusting for age, sex, ethnicity, body mass index and waist-to-hip ratio. Vertex-based associations in the 3D liver mesh were extracted and threshold-free cluster enhancement was applied to improve the sensitivity and stability of the statistical parametric maps. Our findings show that the 3D liver meshes are a robust method for modeling the association between anatomical, anthropometric, and phenotypic variations across the liver. This approach may be readily applied to different clinical conditions as well as extended to other abdominal organs in a larger population.

Keywords

Registration Surface-to-surface Morphology Magnetic resonance imaging 

Notes

Acknowledgements

This research has been conducted using the UK Biobank Resource under Application Number ‘44584’ and was funded by Calico Life Sciences LLC.

References

  1. 1.
    Avants, B.B., Epstein, C., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12, 1361–8415 (2008).  https://doi.org/10.1016/j.media.2007.06.004CrossRefGoogle Scholar
  2. 2.
    Avants, B.B., Tustison, N.J., Song, G., Cook, P.A., Klein, A., Gee, J.C.: A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage 54, 2033–2044 (2011).  https://doi.org/10.1016/j.neuroimage.2010.09.025CrossRefGoogle Scholar
  3. 3.
    Avants, B.B., et al.: The optimal template effect in hippocampus studies of diseased populations. Neuroimage 49, 2457–2466 (2010).  https://doi.org/10.1016/j.neuroimage.2009.09.062CrossRefGoogle Scholar
  4. 4.
    Bagur, A.T., Ridgway, G., McGonigle, J., Brady, S.M., Bulte, D.: Pancreas Segmentation-Derived Biomarkers: Volume and Shape Metrics in the UK Biobank Imaging Study. In: Papież, B.W., Namburete, A.I.L., Yaqub, M., Noble, J.A. (eds.) MIUA 2020. CCIS, vol. 1248, pp. 131–142. Springer, Cham (2020).  https://doi.org/10.1007/978-3-030-52791-4_11CrossRefGoogle Scholar
  5. 5.
    Bai, W., et al.: A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion. Med. Image Anal. 26(1), 133–145 (2015).  https://doi.org/10.1016/j.media.2015.08.009CrossRefGoogle Scholar
  6. 6.
    Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. Methodological 57, 289–300 (1995).  https://doi.org/10.1111/j.2517-6161.1995.tb02031.xMathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Beygelzimer, A., Kakadet, S., Langford, J., Arya, S., Moun, D., Li, S.: Fast Nearest Neighbor Search Algorithms and Applications (2019). https://rdrr.io/cran/FNN. R package version 1.1.3
  8. 8.
    Biffi, C.: An introduction to mass univariate analysis of three-dimensional phenotypes (2017). https://github.com/UK-Digital-Heart-Project/mutools3D. R package version 1.0
  9. 9.
    Biffi, C., et al.: Three-dimensional cardiovascular imaging-genetics: a mass univariate framework. Bioinformatics 34, 97–103 (2018).  https://doi.org/10.1093/bioinformatics/btx552CrossRefGoogle Scholar
  10. 10.
    Bycroft, C., et al.: The UK Biobank resource with deep phenotyping and genomic data. Nature 562(7726), 203–209 (2018).  https://doi.org/10.1038/s41586-018-0579-zCrossRefGoogle Scholar
  11. 11.
    Couinaud, C.: Le Foie: Études Anatomiques et Chirurgicales. Masson, Paris (1957)Google Scholar
  12. 12.
    Dixon, W.T.: Simple proton spectroscopic imaging. Radiology 153(1), 189–194 (1984).  https://doi.org/10.1148/radiology.153.1.6089263CrossRefGoogle Scholar
  13. 13.
    Freedman, D., Lane, D.: A nonstochastic interpretation of reported significance levels. J. Bus. Econ. Stat. 1, 292–298 (1983).  https://doi.org/10.1080/07350015.1983.10509354CrossRefGoogle Scholar
  14. 14.
    Guillaume, B., et al.: Improving mass-univariate analysis of neuroimaging data by modelling important unknown covariates: application to epigenome-wide association studies. NeuroImage 173, 57–71 (2018).  https://doi.org/10.1016/j.neuroimage.2018.01.073CrossRefGoogle Scholar
  15. 15.
    Kühn, J.P., et al.: Prevalence of fatty liver disease and hepatic iron overload in a Northeastern German population by using quantitative MR imaging. Radiology 284, 706–716 (2017).  https://doi.org/10.1148/radiol.2017161228CrossRefGoogle Scholar
  16. 16.
    Littlejohns, T.J., et al.: The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions. Nat. Commun. 11(1), 1–12 (2020).  https://doi.org/10.1038/s41467-020-15948-9CrossRefGoogle Scholar
  17. 17.
    Liu, Y., et al.: Genetic architecture of 11 abdominal organ traits derived from abdominal MRI using deep learning. eLife 10, e65554 (2021)Google Scholar
  18. 18.
    de Marvao, A., et al.: Outcomes and phenotypic expression of rare variants in hypertrophic cardiomyopathy genes amongst UK Biobank participants. medRxiv (2021).  https://doi.org/10.1101/2021.01.21.21249470
  19. 19.
    Medrano-Gracia, P., et al.: Left ventricular shape variation in asymptomatic populations: the multi-ethnic study of atherosclerosis. J. Cardiovasc. Magn. Reson. 16, 56 (2014).  https://doi.org/10.1186/s12968-014-0056-2CrossRefGoogle Scholar
  20. 20.
    Penny, W., Friston, K., Ashburner, J., Kiebel, S., Nichols, T.: Statistical Parametric Mapping: The Analysis of Functional Brain Images. Elsevier/Academic Press, Amsterdam, Boston (2007).  https://doi.org/10.1016/B978-0-12-372560-8.X5000-1
  21. 21.
    R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2020). https://www.R-project.org
  22. 22.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015, Part III. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
  23. 23.
    Schlager, S., Francois, G.: Manipulations of Triangular Meshes Based on the ‘VCGLIB’ API (2021). https://github.com/zarquon42b/Rvcg. R package version 0.19.2
  24. 24.
    Smith, S.M., Nichols, T.E.: Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. NeuroImage 4, 83–98 (2009).  https://doi.org/10.1016/j.neuroimage.2008.03.061CrossRefGoogle Scholar
  25. 25.
    Thomas, E.L., Fitzpatrick, J., Frost, G.S., Bell, J.D.: Metabolic syndrome, overweight and fatty liver. In: Berdanier, C., Dwyer, J., Heber, D. (eds.) Handbook of Nutrition and Food, pp. 763–768. CRC Press, Boca Raton, USA, 3rd edn. (2013).  https://doi.org/10.1201/b15294
  26. 26.
    Winkler, A.M., Ridgway, G.R., Webster, M.A., Smith, S.M., Nichols, T.E.: Permutation inference for the general linear model. NeuroImage 92, 381–397 (2014).  https://doi.org/10.1016/j.neuroimage.2014.01.060CrossRefGoogle Scholar
  27. 27.
    Younossi, Z.M.: Non-alcoholic fatty liver disease - a global public health perspective. J. Hepatol. 70(3), 531–544 (2019).  https://doi.org/10.1016/j.jhep.2018.10.033CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Research Centre for Optimal Health, School of Life SciencesUniversity of WestminsterLondonUK
  2. 2.Calico Life Sciences LLCSouth San FranciscoUSA

Personalised recommendations