Abstract
The reconstruction of cerebral cortex surfaces from brain MRI scans is instrumental for the analysis of brain morphology and the detection of cortical thinning in neurodegenerative diseases like Alzheimer’s disease (AD). Moreover, for a fine-grained analysis of atrophy patterns, the parcellation of the cortical surfaces into individual brain regions is required. For the former task, powerful deep learning approaches, which provide highly accurate brain surfaces of tissue boundaries from input MRI scans in seconds, have recently been proposed. However, these methods do not come with the ability to provide a parcellation of the reconstructed surfaces. Instead, separate brain-parcellation methods have been developed, which typically consider the cortical surfaces as given, often computed beforehand with FreeSurfer. In this work, we propose two options, one based on a graph classification branch and another based on a novel generic 3D reconstruction loss, to augment template-deformation algorithms such that the surface meshes directly come with an atlas-based brain parcellation. By combining both options with two of the latest cortical surface reconstruction algorithms, we attain highly accurate parcellations with a Dice score of 90.2 (graph classification branch) and 90.4 (novel reconstruction loss) together with state-of-the-art surfaces.
A.-M. Rickmann and F. Bongratz—Equal contribution.
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
Notes
- 1.
Available at https://surfer.nmr.mgh.harvard.edu/.
References
Bongratz, F., Rickmann, A.M., Pölsterl, S., Wachinger, C.: Vox2cortex: fast explicit reconstruction of cortical surfaces from 3D MRI scans with geometric deep neural networks. In: CVPR (2022)
Cignoni, P., Callieri, M., Corsini, M., Dellepiane, M., Ganovelli, F., Ranzuglia, G.: MeshLab: an open-source mesh processing tool. In: Eurographics Italian Chapter Conference. The Eurographics Association (2008)
Coupé, P., et al.: Assemblynet: a large ensemble of CNNs for 3Dd whole brain MRI segmentation. NeuroImage 219, 117026 (2020)
Cruz, R.S., Lebrat, L., Bourgeat, P., Fookes, C., Fripp, J., Salvado, O.: Deepcsr: a 3D deep learning approach for cortical surface reconstruction. In: WACV, pp. 806–815 (2021)
Cucurull, G., et al.: Convolutional neural networks for mesh-based parcellation of the cerebral cortex (2018)
Desikan, R.S., et al.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31(3), 968–980 (2006)
Destrieux, C., Fischl, B., Dale, A., Halgren, E.: Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. NeuroImage 53(1), 1–15 (2010)
Eschenburg, K.M., Grabowski, T.J., Haynor, D.R.: Learning cortical parcellations using graph neural networks. Front. Neurosci. 15 (2021)
van Essen, D.C., Glasser, M.F., Dierker, D.L., Harwell, J.W., Coalson, T.S.: Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases. Cerebral Cortex 22(10), 2241–2262 (2012)
Fischl, B.: Freesurfer. Neuroimage 62(2), 774–781 (2012)
Garland, M., Heckbert, P.S.: Surface simplification using quadric error metrics. In: Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH 1997), USA, pp. 209–216 (1997)
Gkioxari, G., Johnson, J., Malik, J.: Mesh r-cnn. In: ICCV, pp. 9784–9794 (2019)
Glasser, M.F., et al.: A multi-modal parcellation of human cerebral cortex. Nature 536(7615), 171–178 (2016)
Gopinath, K., Desrosiers, C., Lombaert, H.: Graph convolutions on spectral embeddings for cortical surface parcellation. Med. Image Anal. 54, 297–305 (2019)
Gopinath, K., Desrosiers, C., Lombaert, H.: SegRecon: learning joint brain surface reconstruction and segmentation from images. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12907, pp. 650–659. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87234-2_61
Henschel, L., Conjeti, S., Estrada, S., Diers, K., Fischl, B., Reuter, M.: Fastsurfer - a fast and accurate deep learning based neuroimaging pipeline. NeuroImage 219, 117012 (2020)
Huo, Y., et al.: 3D whole brain segmentation using spatially localized atlas network tiles. NeuroImage 194, 105–119 (2019)
Klein, A., Tourville, J.: 101 labeled brain images and a consistent human cortical labeling protocol. Front. Neurosci. 6 (2012)
Kong, F., Shadden, S.C.: Whole heart mesh generation for image-based computational simulations by learning free-from deformations (2021)
Lebrat, L., et al.: Corticalflow: a diffeomorphic mesh transformer network for cortical surface reconstruction. Adv. Neural Inf. Process. Syst. 34 (2021)
Lewiner, T., Lopes, H., Vieira, A.W., Tavares, G.: Efficient implementation of marching cubes’ cases with topological guarantees. J. Graph. Tools 8(2), 1–15 (2003)
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: International Conference on Learning Representations (2019)
Ma, Q., Robinson, E.C., Kainz, B., Rueckert, D., Alansary, A.: PialNN: a fast deep learning framework for cortical pial surface reconstruction. In: Abdulkadir, A., et al. (eds.) MLCN 2021. LNCS, vol. 13001, pp. 73–81. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87586-2_8
Mai, J.K., Paxinos, G.: The Human Nervous System. Academic Press (2011)
Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J. Cognit. Neurosci. 19(9), 1498–1507 (2007)
Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 32, 8024–8035 (2019)
Ravi, N., et al.: Accelerating 3d deep learning with pytorch3d. arXiv:2007.08501 (2020)
Roe, J.M., et al.: Asymmetric thinning of the cerebral cortex across the adult lifespan is accelerated in Alzheimer’s disease. Nat. Commun. 12(1) (2021)
Smith, L.N.: Cyclical learning rates for training neural networks. In: WACV, pp. 464–472 (2017)
Wang, N., Zhang, Y., Li, Z., Fu, Y., Liu, W., Jiang, Y.-G.: Pixel2Mesh: generating 3D mesh models from single RGB images. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 55–71. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01252-6_4
Wickramasinghe, U., Remelli, E., Knott, G., Fua, P.: Voxel2Mesh: 3D mesh model generation from volumetric data. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 299–308. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59719-1_30
Acknowledgments
This research was partially supported by the Bavarian State Ministry of Science and the Arts and coordinated by the bidt, and the BMBF (DeepMentia, 031L0200A). We gratefully acknowledge the computational resources provided by the Leibniz Supercomputing Centre (www.lrz.de).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Rickmann, AM., Bongratz, F., Pölsterl, S., Sarasua, I., Wachinger, C. (2022). Joint Reconstruction and Parcellation of Cortical Surfaces. In: Abdulkadir, A., et al. Machine Learning in Clinical Neuroimaging. MLCN 2022. Lecture Notes in Computer Science, vol 13596. Springer, Cham. https://doi.org/10.1007/978-3-031-17899-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-17899-3_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-17898-6
Online ISBN: 978-3-031-17899-3
eBook Packages: Computer ScienceComputer Science (R0)
