Abstract
As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30–70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.
D. Petrov and B.A. Gutman—These authors contributed equally.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Thompson, P.M., Andreassen, O.A., Arias-Vasquez, A., Bearden, C.E., Boedhoe, P.S., Brouwer, R.M., Buckner, R.L., Buitelaar, J.K., Bulaeva, K.B., Cannon, D.M.: Enigma and the individual: predicting factors that affect the brain in 35 countries worldwide. Neuroimage 145(Pt B), 389–408 (2015)
Fischl, B.: Freesurfer. Neuroimage 62(2), 774–781 (2012)
Gutman, B., Ching, C., Andreassen, O., Schmaal, L., Veltman, D., Van Erp, T., Turner, J., Thompson, P.M., et al.: Harmonized large-scale anatomical shape analysis: mapping subcortical differences across the enigma bipolar, schizophrenia, and major depression working groups. Biol. Psychiatry 81(10), S308 (2017)
Gutman, B.A., Jahanshad, N., Ching, C.R., Wang, Y., Kochunov, P.V., Nichols, T.E., Thompson, P.M.: Medial demons registration localizes the degree of genetic influence over subcortical shape variability: An n = 1480 meta-analysis. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp. 1402–1406. IEEE (2015)
Roshchupkin, G.V., Gutman, B.A., et al.: Heritability of the shape of subcortical brain structures in the general population. Nat. Commun. 7, 13738 (2016)
Gutman, B.A., Madsen, S.K., Toga, A.W., Thompson, P.M.: A family of fast spherical registration algorithms for cortical shapes. In: Shen, L., Liu, T., Yap, P.-T., Huang, H., Shen, D., Westin, C.-F. (eds.) MBIA 2013. LNCS, vol. 8159, pp. 246–257. Springer, Cham (2013). doi:10.1007/978-3-319-02126-3_24
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 785–794. ACM, New York (2016)
Pedregosa, F., Varoquaux, G., Gramfort, A., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
van Erp, T.G.M., Hibar, D.P., et al.: Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the enigma consortium. Mol. Psychiatry 21, 547–553 (2015)
Schmaal, L., Hibar, D.P., et al.: Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the enigma major depressive disorder working group. Mol. Psychiatry 22(6), 900–909 (2017)
Esteban, O., Birman, D., Schaer, M., Koyejo, O.O., Poldrack, R.A., Gorgolewski, K.J.: Mriqc: predicting quality in manual MRI assessment protocols using no-reference image quality measures. bioRxiv (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Petrov, D. et al. (2017). Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging. In: Wang, Q., Shi, Y., Suk, HI., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2017. Lecture Notes in Computer Science(), vol 10541. Springer, Cham. https://doi.org/10.1007/978-3-319-67389-9_43
Download citation
DOI: https://doi.org/10.1007/978-3-319-67389-9_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67388-2
Online ISBN: 978-3-319-67389-9
eBook Packages: Computer ScienceComputer Science (R0)