Abstract
Being able to adequately process and combine data arising from different sites is crucial in neuroimaging, but is difficult, owing to site, sequence and acquisition-parameter dependent biases. It is important therefore to design algorithms that are not only robust to images of differing contrasts, but also be able to generalise well to unseen ones, with a quantifiable measure of uncertainty. In this paper we demonstrate the efficacy of a physics-informed, uncertainty-aware, segmentation network that employs augmentation-time MR simulations and homogeneous batch feature stratification to achieve acquisition invariance. We show that the proposed approach also accurately extrapolates to out-of-distribution sequence samples, providing well calibrated volumetric bounds on these. We demonstrate a significant improvement in terms of coefficients of variation, backed by uncertainty based volumetric validation.
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References
Project MONAI. https://doi.org/10.5281/zenodo.4323059
Ashburner, J., Friston, K.J.: Unified segmentation. Neuroimage 26(3), 839–851 (2005). https://doi.org/10.1016/j.neuroimage.2005.02.018
Billot, B., Greve, D., Van Leemput, K., Fischl, B., Iglesias, J.E., Dalca, A.V.: A learning strategy for contrast-agnostic MRI segmentation. arXiv (2020)
Borges, P., et al.: Physics-informed brain MRI segmentation. In: Burgos, N., Gooya, A., Svoboda, D. (eds.) SASHIMI 2019. LNCS, vol. 11827, pp. 100–109. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32778-1_11
Papież, B.W., Namburete, A.I.L., Yaqub, M., Noble, J.A. (eds.): MIUA 2020. CCIS, vol. 1248. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52791-4
Eaton-Rosen, Z., Bragman, F., Bisdas, S., Ourselin, S., Cardoso, M.J.: Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 691–699. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_78
Fortin, J.P., et al.: Harmonization of cortical thickness measurements across scanners and sites. NeuroImage 167, 104–120 (2018). https://doi.org/10.1016/j.neuroimage.2017.11.024
Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: 33rd International Conference on Machine Learning, ICML 2016, vol. 3, pp. 1651–1660 (2015)
Helms, G., et al.: Increased SNR and reduced distortions by averaging multiple gradient echo signals in 3D flash imaging of the human brain at 3T. J. Magn. Reson. Imaging Off. J. Int. Soc. Magn. Reson. Med. 29(1), 198–204 (2009)
Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: No New-Net. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 234–244. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_21
Jog, A., Carass, A., Roy, S., Pham, D.L., Prince, B., Amod, J.L.: MR image synthesis by contrast learning on neighborhood ensembles. Med. Image Anal. 24, 63–76 (2015). https://doi.org/10.1016/j.media.2015.05.002
Jog, A., Hoopes, A., Greve, D.N., Van Leemput, K., Fischl, B.: PSACNN: pulse sequence adaptive fast whole brain segmentation. NeuroImage 199, 553–569 (2019)
Johnson, W.E., Li, C., Rabinovic, A.: Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8(1), 118–127 (2007). https://doi.org/10.1093/biostatistics/kxj037
Kendall, A., Gal, Y.: What uncertainties do we need in bayesian deep learning for computer vision? Technical report (2017)
Pérez-García, F., Sparks, R., Ourselin, S.: TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning. Comput. Methods Prog. Biomed. 208, 106236 (2020)
Pham, D.L., Chou, Y.-Y., Dewey, B.E., Reich, D.S., Butman, J.A., Roy, S.: Contrast adaptive tissue classification by alternating segmentation and synthesis. In: Burgos, N., Svoboda, D., Wolterink, J.M., Zhao, C. (eds.) SASHIMI 2020. LNCS, vol. 12417, pp. 1–10. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59520-3_1
Sabuncu, M.R., Yeo, B.T., Leemput, K.V., Fischl, B., Golland, P.: A generative model for image segmentation based on label fusion. IEEE Trans. Med. Imaging 29, 1714–1729 (2010). https://doi.org/10.1109/TMI.2010.2050897. https://pubmed.ncbi.nlm.nih.gov/20562040/
Srivastava, N., Hinton, G., Krizhevsky, A., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. Technical report 56 (2014)
Zhao, F., et al.: Harmonization of infant cortical thickness using surface-to-surface cycle-consistent adversarial networks. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 475–483. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_52
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision 2017, October, pp. 2242–2251 (2017)
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This project was funded by the Wellcome Flagship Programme (WT213038/Z/18/Z) and Wellcome EPSRC CME (WT203148/Z/16/Z).
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Borges, P. et al. (2021). The Role of MRI Physics in Brain Segmentation CNNs: Achieving Acquisition Invariance and Instructive Uncertainties. In: Svoboda, D., Burgos, N., Wolterink, J.M., Zhao, C. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2021. Lecture Notes in Computer Science(), vol 12965. Springer, Cham. https://doi.org/10.1007/978-3-030-87592-3_7
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