Abstract
Longitudinal infant dedicated cerebellum atlases play a fundamental role in characterizing and understanding the dynamic cerebellum development during infancy. However, due to the limited spatial resolution, low tissue contrast, tiny folding structures, and rapid growth of the cerebellum during this stage, it is challenging to build such atlases while preserving clear folding details. Furthermore, the existing atlas construction methods typically independently build discrete atlases based on samples for each age group without considering the within-subject temporal consistency, which is critical for large-scale longitudinal studies. To fill this gap, we propose an age-conditional multi-stage learning framework to construct longitudinally consistent 4D infant cerebellum atlases. Specifically, 1) A joint affine and deformable atlas construction framework is proposed to accurately build temporally continuous atlases based on the entire cohort, and rapidly warp the new images to the atlas space; 2) A longitudinal constraint is employed to enforce the within-subject temporal consistency during atlas building; 3) A Correntropy based regularization loss is further exploited to enhance the robustness of our framework. Our atlases are constructed based on 405 longitudinal scans from 187 healthy infants with age ranging from 6 to 27 months, and are compared to the atlases built by state-of-the-art algorithms. Results demonstrate that our atlases preserve more structural details and fine-grained cerebellum folding patterns, which ensure higher accuracy in subsequent atlas-based registration and segmentation tasks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Robinson, E.C., et al.: MSM: a new flexible framework for multimodal surface matching. Neuroimage 100, 414–426 (2014)
Kase, C., Norrving, B., Levine, S., et al.: Cerebellar infarction. Clinical and anatomic observations in 66 cases. Stroke 24(1), 76–83 (1993)
Davie, C., Barker, G., Webb, S., et al.: Persistent functional deficit in multiple sclerosis and autosomal dominant cerebellar ataxia is associated with axon loss. Brain 118(6), 1583–1592 (1995)
Klockgether, T.: The clinical diagnosis of autosomal dominant spinocerebellar ataxias. Cerebellum 7(2), 101 (2008)
Desmond, J.E., Gabrieli, J.D., Glover, G.H.: Dissociation of frontal and cerebellar activity in a cognitive task: evidence for a distinction between selection and search. Neuroimage 7(4), 368–376 (1998)
Riva, D., Giorgi, C.: The cerebellum contributes to higher functions during development: evidence from a series of children surgically treated for posterior fossa tumours. Brain 123(5), 1051–1061 (2000)
Stoodley, C.J., Schmahmann, J.D.: Functional topography in the human cerebellum: a meta-analysis of neuroimaging studies. Neuroimage 44(2), 489–501 (2009)
Seidman, L.J., Valera, E.M., Makris, N.: Structural brain imaging of attention-deficit/hyperactivity disorder. Biol. Psychiatry 57(11), 1263–1272 (2005)
Bishop, D.V.: Cerebellar abnormalities in developmental dyslexia: cause, correlate or consequence? (2002)
Courchesne, E., Saitoh, O., Yeung-Courchesne, R., et al.: Abnormality of cerebellar Vermian lobules VI and VII in patients with infantile autism: identification of hypoplastic and hyperplastic subgroups with MR imaging. 162(1), 123–130 (1994)
Knickmeyer, R.C., Gouttard, S., Kang, C., et al.: A structural MRI study of human brain development from birth to 2 years. J. Neurosci. 28(47), 12176–12182 (2008)
Diedrichsen, J., Balsters, J.H., Flavell, J., Cussans, E., Ramnani, N.: A probabilistic MR atlas of the human cerebellum. Neuroimage 46(1), 39–46 (2009)
Diedrichsen, J.: A spatially unbiased atlas template of the human cerebellum. Neuroimage 33(1), 127–138 (2006)
Schmahmann, J.D., Doyon, J., McDonald, D., et al.: Three-dimensional MRI atlas of the human cerebellum in proportional stereotaxic space. Neuroimage 10(3), 233–260 (1999)
Avants, B.B., et al.: The optimal template effect in hippocampus studies of diseased populations. Neuroimage 49(3), 2457–2466 (2010)
Schuh, A., Makropoulos, A., Robinson, E.C., et al.: Unbiased construction of a temporally consistent morphological atlas of neonatal brain development. bioRxiv (2018) 251512
Shi, F., et al.: Infant brain atlases from neonates to 1- and 2-year-olds. PloS ONE 6(4), e18746 (2011)
Zhang, Y., Shi, F., Wu, G., Wang, L., Yap, P.T., Shen, D.: Consistent spatial-temporal longitudinal atlas construction for developing infant brains. IEEE TMI 35(12), 2568–2577 (2016)
Dalca, A.V., Balakrishnan, G., Guttag, J., Sabuncu, M.R.: Unsupervised learning for fast probabilistic diffeomorphic registration. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 729–738. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_82
Fan, J., Cao, X., Xue, Z., Yap, P.T., Shen, D.: Adversarial similarity network for evaluating image alignment in deep learning based registration. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 739–746. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_83
Fan, J., Cao, X., Wang, Q., Yap, P.T., Shen, D.: Adversarial learning for mono-or multi-modal registration. Med. Image Anal. 58, 101545 (2019)
Shen, Z., Han, X., Xu, Z., Niethammer, M.: Networks for joint affine and non-parametric image registration. In: CVPR, pp. 4224–4233 (2019)
Wei, D., et al.: Deep morphological simplification network (MS-Net) for guided registration of brain magnetic resonance images. Pattern Recogn. 100, 107171 (2020)
Dalca, A.V., Rakic, M., Guttag, J., Sabuncu, M.R.: Learning conditional deformable templates with convolutional networks. In: NeurIPS (2019)
Hill, J., et al.: A surface-based analysis of hemispheric asymmetries and folding of cerebral cortex in term-born human infants. J. Neurosci. 30(6), 2268–2276 (2010)
Li, G., et al.: Mapping region-specific longitudinal cortical surface expansion from birth to 2 years of age. Cereb. Cortex 23(11), 2724–2733 (2013)
Triulzi, F., Parazzini, C., Righini, A.: MRI of fetal and neonatal cerebellar development. In: Seminars in Fetal and Neonatal Medicine, vol. 10, pp. 411–420. Elsevier (2005)
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Krebs, J., Mansi, T., Mailhé, B., Ayache, N., Delingette, H.: Unsupervised probabilistic deformation modeling for robust diffeomorphic registration. In: Stoyanov, D., et al. (eds.) DLMIA 2018, ML-CDS 2018. LNCS, vol. 11045, pp. 101–109. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_12
Chen, L., Qu, H., Zhao, J., Chen, B., Principe, J.C.: Efficient and robust deep learning with correntropy-induced loss function. Neural Comput. Appl. 27(4), 1019–1031 (2016)
de Vos, B.D., Berendsen, F.F., Viergever, M.A., Sokooti, H., Staring, M., Išgum, I.: A deep learning framework for unsupervised affine and deformable image registration. Med. Image Anal. 52, 128–143 (2019)
Howell, B.R., et al.: The UNC/UMN baby connectome project (BCP): an overview of the study design and protocol development. NeuroImage 185, 891–905 (2019)
Avants, B., Gee, J.C.: Geodesic estimation for large deformation anatomical shape averaging and interpolation. Neuroimage 23, S139–S150 (2004)
Acknowledgments
This work was partially supported by NIH grants (MH116225, MH109773, MH117943, and MH123202). This work also utilizes approaches developed by an NIH grant (1U01MH110274) and the efforts of the UNC/UMN Baby Connectome Project Consortium.
Author information
Authors and Affiliations
Consortia
Corresponding authors
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, L. et al. (2021). Construction of Longitudinally Consistent 4D Infant Cerebellum Atlases Based on Deep Learning. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12904. Springer, Cham. https://doi.org/10.1007/978-3-030-87202-1_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-87202-1_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87201-4
Online ISBN: 978-3-030-87202-1
eBook Packages: Computer ScienceComputer Science (R0)