Abstract
Segmentation of the developing fetal brain is an important step in quantitative analyses. However, manual segmentation is a very time-consuming task which is prone to error and must be completed by highly specialized individuals. Super-resolution reconstruction of fetal MRI has become standard for processing such data as it improves image quality and resolution. However, different pipelines result in slightly different outputs, further complicating the generalization of segmentation methods aiming to segment super-resolution data. Therefore, we propose using transfer learning with noisy multi-class labels to automatically segment high resolution fetal brain MRIs using a single set of segmentations created with one reconstruction method and tested for generalizability across other reconstruction methods. Our results show that the network can automatically segment fetal brain reconstructions into 7 different tissue types, regardless of reconstruction method used. Transfer learning offers some advantages when compared to training without pre-initialized weights, but the network trained on clean labels had more accurate segmentations overall. No additional manual segmentations were required. Therefore, the proposed network has the potential to eliminate the need for manual segmentations needed in quantitative analyses of the fetal brain independent of reconstruction method used, offering an unbiased way to quantify normal and pathological neurodevelopment.
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References
Kuklisova-Murgasova, M., Quaghebeur, G., Rutherford, M.A., Hajnal, J.V., Schnabel, J.A.: Reconstruction of fetal brain MRI with intensity matching and complete outlier removal. Med. Image Anal. 16, 1550–1564 (2012). https://doi.org/10.1016/j.media.2012.07.004
Kainz, B., et al.: Fast volume reconstruction from motion corrupted stacks of 2D slices. IEEE Trans. Med. Imaging 34, 1901–1913 (2015). https://doi.org/10.1109/TMI.2015.2415453
Ebner, M., et al.: An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI. NeuroImage 206, 116324 (2020). https://doi.org/10.1016/j.neuroimage.2019.116324
Tourbier, S., Bresson, X., Hagmann, P., Thiran, J.-P., Meuli, R., Cuadra, M.B.: An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization. NeuroImage 118, 584–597 (2015). https://doi.org/10.1016/j.neuroimage.2015.06.018
Jiang, S., Xue, H., Glover, A., Rutherford, M., Rueckert, D., Hajnal, J.V.: MRI of moving subjects using multislice snapshot images with volume reconstruction (SVR): application to fetal, neonatal, and adult brain studies. IEEE Trans. Med. Imaging 26, 967–980 (2007). https://doi.org/10.1109/TMI.2007.895456
Rousseau, F., et al.: Registration-based approach for reconstruction of high-resolution in utero fetal MR brain images. Acad. Radiol. 13, 1072–1081 (2006). https://doi.org/10.1016/j.acra.2006.05.003
Kim, K., Habas, P.A., Rousseau, F., Glenn, O.A., Barkovich, A.J., Studholme, C.: Intersection based motion correction of multislice MRI for 3-D in utero fetal brain image formation. IEEE Trans. Med. Imaging 29, 146–158 (2010). https://doi.org/10.1109/TMI.2009.2030679
Tourbier, S., et al.: Automated template-based brain localization and extraction for fetal brain MRI reconstruction. NeuroImage 155, 460–472 (2017). https://doi.org/10.1016/j.neuroimage.2017.04.004
Wright, R., et al.: Automatic quantification of normal cortical folding patterns from fetal brain MRI. NeuroImage 91, 21–32 (2014). https://doi.org/10.1016/j.neuroimage.2014.01.034
Keraudren, K., et al.: Automated fetal brain segmentation from 2D MRI slices for motion correction. NeuroImage. 101, 633–643 (2014). https://doi.org/10.1016/j.neuroimage.2014.07.023
Salehi, S.S.M., et al.: Real-time automatic fetal brain extraction in fetal MRI by deep learning. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 720–724 (2018). https://doi.org/10.1109/ISBI.2018.8363675
Khalili, N., et al.: Automatic brain tissue segmentation in fetal MRI using convolutional neural networks. Magn. Reson. Imaging (2019). https://doi.org/10.1016/j.mri.2019.05.020
Gholipour, A., Akhondi-Asl, A., Estroff, J.A., Warfield, S.K.: Multi-atlas multi-shape segmentation of fetal brain MRI for volumetric and morphometric analysis of ventriculomegaly. NeuroImage 60, 1819–1831 (2012). https://doi.org/10.1016/j.neuroimage.2012.01.128
Payette, K., et al.: Longitudinal analysis of fetal MRI in patients with prenatal spina bifida repair. In: Wang, Q., et al. (eds.) PIPPI/SUSI - 2019. LNCS, vol. 11798, pp. 161–170. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32875-7_18
Gholipour, A., et al.: A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth. Sci. Rep. 7 (2017). https://doi.org/10.1038/s41598-017-00525-w
Habas, P.A., Kim, K., Rousseau, F., Glenn, O.A., Barkovich, A.J., Studholme, C.: Atlas-based segmentation of developing tissues in the human brain with quantitative validation in young fetuses. Hum. Brain Mapp. 31, 1348–1358 (2010). https://doi.org/10.1002/hbm.20935
Yu, X., Liu, T., Gong, M., Zhang, K., Batmanghelich, K., Tao, D.: Transfer Learning with Label Noise (2017)
Moosavi-Dezfooli, S., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 86–94 (2017). https://doi.org/10.1109/CVPR.2017.17
Karimi, D., Dou, H., Warfield, S.K., Gholipour, A.: Deep learning with noisy labels: exploring techniques and remedies in medical image analysis (2019)
Ghosh, A., Kumar, H., Sastry, P.S.: Robust loss functions under label noise for deep neural networks. In: AAAI. AAAI Publications (2017)
Cheplygina, V., de Bruijne, M., Pluim, J.P.W.: Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. Med. Image Anal. 54, 280–296 (2019). https://doi.org/10.1016/j.media.2019.03.009
Deman, P., Tourbier, S., Meuli, R., Cuadra, M.B.: meribach/mevislabFetalMRI: MEVISLAB MIAL Super-Resolution Reconstruction of Fetal Brain MRI v1.0. Zenodo (2020). https://doi.org/10.5281/zenodo.3878564
Jenkinson, M., Bannister, P., Brady, M., Smith, S.: Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17, 825–841 (2002). https://doi.org/10.1016/s1053-8119(02)91132-8
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
Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS - 2017. LNCS, vol. 10553, pp. 240–248. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_28
Avants, B.B., Tustison, N., Song, G.: Advanced normalization tools (ANTS). Insight J. 2, 1–35 (2009)
Taha, A.A., Hanbury, A.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15 (2015). https://doi.org/10.1186/s12880-015-0068-x
Acknowledgements
Financial support was provided by the OPO Foundation, Anna Müller Grocholski Foundation, the Foundation for Research in Science and the Humanities at the University of Zurich, EMDO Foundation, Hasler Foundation, the Forschungszentrum für das Kind Grant (FZK) and the PhD Grant from the Neuroscience Center Zurich.
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Payette, K., Kottke, R., Jakab, A. (2020). Efficient Multi-class Fetal Brain Segmentation in High Resolution MRI Reconstructions with Noisy Labels. In: Hu, Y., et al. Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis. ASMUS PIPPI 2020 2020. Lecture Notes in Computer Science(), vol 12437. Springer, Cham. https://doi.org/10.1007/978-3-030-60334-2_29
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