Abstract
Deep learning strategies have become ubiquitous optimization tools for medical image analysis. With the appropriate amount of data, these approaches outperform classic methodologies in a variety of image processing tasks. However, rare diseases and pediatric imaging often lack extensive data. Specially, MRI are uncommon because they require sedation in young children. Moreover, the lack of standardization in MRI protocols introduces a strong variability between different datasets. In this paper, we present a general deep learning architecture for MRI homogenization that also provides the segmentation map of an anatomical region of interest. Homogenization is achieved using an unsupervised architecture based on variational autoencoder with cycle generative adversarial networks, which learns a common space (i.e. a representation of the optimal imaging protocol) using an unpaired image-to-image translation network. The segmentation is simultaneously generated by a supervised learning strategy. We evaluated our method segmenting the challenging anterior visual pathway using three brain T1-weighted MRI datasets (variable protocols and vendors). Our method significantly outperformed a non-homogenized multi-protocol U-Net.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Collewet, G., Strzelecki, M., Mariette, F.: Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn. Reson. Imaging 22, 81–91 (2004)
Vovk, U., Pernus, F., Likar, B.: A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans. Med. Imaging 26, 405–421 (2007)
Nyul, L.G., Udupa, J.K., Zhang, X.: New variants of a method of MRI scale standardization. IEEE Trans. Med. Imaging 19, 143–150 (2000)
Shah, M., et al.: Evaluating intensity normalization on MRIs of human brain with multiple sclerosis. Med. Image Anal. 15, 267–282 (2011)
Sun, X., Shi, L., Luo, Y., Yang, W., Li, H., Liang, P., et al.: Histogram-based normalization technique on human brain magnetic resonance images from different acquisitions. BioMed. Eng. OnLine. 14, 73 (2015)
Jager, F., Hornegger, J.: Nonrigid registration of joint histograms for intensity standardization in magnetic resonance imaging. IEEE Trans. Med. Imaging 28, 137–150 (2009)
Roy, S., Carass, A., Prince, J.L.: Patch based intensity normalization of brain MR images. In: 2013 IEEE 10th International Symposium on Biomedical Imaging, pp. 342–345. IEEE (2013)
Shinohara, R.T., et al.: Statistical normalization techniques for magnetic resonance imaging. Neuroimage Clin. 6, 9–19 (2014)
Zhang, J., Saha, A., Soher, B.J., Mazurowski, M.A.: Automatic deep learning-based normalization of breast dynamic contrast-enhanced magnetic resonance images. (2018), arXiv:1807.02152 [cs]
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. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Simkó, A., Löfstedt, T., Garpebring, A., Nyholm, T., Jonsson, J.: A Generalized Network for MRI Intensity Normalization. (2019), arXiv:1909.05484 [eess]
Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29, 1310–1320 (2010)
Jacobsen, N., Deistung, A., Timmann, D., Goericke, S.L., Reichenbach, J.R., Güllmar, D.: Analysis of intensity normalization for optimal segmentation performance of a fully convolutional neural network. Z. Med. Phys. 29, 128–138 (2019)
Liu, M.-Y., Tuzel, O.: Coupled generative adversarial networks. In: Advances in Neural Information Processing Systems, Curran Associates, Inc. 29, pp. 469–477 (2016)
Zhu, J.-Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Presented at the Proceedings of the IEEE International Conference on Computer Vision, IEEE (2017)
Liu, M.-Y., Breuel, T., Kautz, J.: Unsupervised image-to-image translation networks. In: Advances in Neural Information Processing Systems, Curran Associates, Inc. 30, pp. 700–708 (2017)
Wolterink, J.M., Dinkla, A.M., Savenije, M.H.F., Seevinck, P.R., van den Berg, C.A.T., Išgum, I.: Deep MR to CT synthesis using unpaired data. In: Tsaftaris, S.A., Gooya, A., Frangi, A.F., Prince, J.L. (eds.) SASHIMI 2017. LNCS, vol. 10557, pp. 14–23. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68127-6_2
Yang, H., et al.: Unpaired brain MR-to-CT synthesis using a structure-constrained cycleGAN. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 174–182. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_20
Welander, P., Karlsson, S., Eklund, A.: Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT. (2018), arXiv:1806.07777 [cs]
Fortin, J.-P., et al.: Harmonization of multi-site diffusion tensor imaging data. NeuroImage. 161, 149–170 (2017)
Blumberg, S.B., Palombo, M., Khoo, C.S., Tax, C.M.W., Tanno, R., Alexander, D.C.: Multi-stage prediction networks for data harmonization. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 411–419. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_45
Moyer, D., Steeg, G.V., Tax, C.M.W., Thompson, P.M.: Scanner invariant representations for diffusion MRI harmonization. Magnetic Resonance in Medicine, (2020)
Koppers, S., Bloy, L., Berman, J.I., Tax, C.M.W., Edgar, J.C., Merhof, D.: Spherical harmonic residual network for diffusion signal harmonization. In: Bonet-Carne, E., Grussu, F., Ning, L., Sepehrband, F., Tax, C.M.W. (eds.) MICCAI 2019. MV, pp. 173–182. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05831-9_14
Noble, J.H., Dawant, B.M.: An atlas-navigated optimal medial axis and deformable model algorithm (NOMAD) for the segmentation of the optic nerves and chiasm in MR and CT images. Med. Image Anal. 15, 877–884 (2011)
Yang, X., et al.: Weighted partitioned active shape model for optic pathway segmentation in MRI. In: Linguraru, M.G., et al. (eds.) CLIP 2014. LNCS, vol. 8680, pp. 109–117. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13909-8_14
Mansoor, A., et al.: Deep learning guided partitioned shape model for anterior visual pathway segmentation. IEEE Trans. Med. Imaging 35, 1856–1865 (2016)
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of wasserstein GANs. In: Advances in Neural Information Processing Systems, Curran Associates, Inc. 30, pp. 5767–5777 (2017)
Fortin, J.-P., et al.: Harmonization of cortical thickness measurements across scanners and sites. NeuroImage. 167, 104–120 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Tor-Diez, C., Porras, A.R., Packer, R.J., Avery, R.A., Linguraru, M.G. (2020). Unsupervised MRI Homogenization: Application to Pediatric Anterior Visual Pathway Segmentation. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds) Machine Learning in Medical Imaging. MLMI 2020. Lecture Notes in Computer Science(), vol 12436. Springer, Cham. https://doi.org/10.1007/978-3-030-59861-7_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-59861-7_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59860-0
Online ISBN: 978-3-030-59861-7
eBook Packages: Computer ScienceComputer Science (R0)