Abstract
We present several deep learning models for assessing the morphometric fidelity of deep grey matter region models extracted from brain MRI. We test three different convolutional neural net architectures (VGGNet, ResNet and Inception) over 2D maps of geometric features. Further, we present a novel geometry feature augmentation technique based on parametric spherical mapping. Finally, we present an approach for model decision visualization, allowing human raters to see the areas of subcortical shapes most likely to be deemed of failing quality by the machine. Our training data is comprised of 5200 subjects from the ENIGMA Schizophrenia MRI cohorts, and our test dataset contains 1500 subjects from the ENIGMA Major Depressive Disorder cohorts. Our final models reduce human rater time by 46–70%. ResNet outperforms VGGNet and Inception for all of our predictive tasks.
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
Gutman, B., 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., et al.: 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). https://doi.org/10.1007/978-3-319-02126-3_24
Gutman, B.A., Yalin, W., Rajagopalan, P., Toga, A.W., Thompson, P.M.: Shape matching with medial curves and 1-D group-wise registration. In: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp.716–719 (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-ResNet and the impact of residual connections on learning. In: AAAI, vol. 4, p. 12 (2017)
Jo, J., Bengio, Y.: Measuring the tendency of CNNs to learn surface statistical regularities. arXiv preprint arXiv:1711.11561 (2017)
Zintgraf, L.M., Cohen, T.S., Adel, T., Welling, M.: Visualizing deep neural network decisions: prediction difference analysis. arXiv preprint arXiv:1702.04595 (2017)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. See https://arxiv.org/abs/1610.02391 v3 7(8) (2016)
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 (2016)
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)
Cohen, T.S., Geiger, M., Koehler, J., Welling, M.: Spherical CNNs. arXiv preprint arXiv:1801.10130 (2018)
Acknowledgements
This work was funded in part by NIH BD2K grant U54 EB020403 and Russian Science Foundation grant 17-11-01390.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Petrov, D. et al. (2018). Deep Learning for Quality Control of Subcortical Brain 3D Shape Models. In: Reuter, M., Wachinger, C., Lombaert, H., Paniagua, B., Lüthi, M., Egger, B. (eds) Shape in Medical Imaging. ShapeMI 2018. Lecture Notes in Computer Science(), vol 11167. Springer, Cham. https://doi.org/10.1007/978-3-030-04747-4_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-04747-4_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04746-7
Online ISBN: 978-3-030-04747-4
eBook Packages: Computer ScienceComputer Science (R0)