Abstract
The infant brain undergoes rapid changes in volume, shape, and structural organization during the first postnatal year. Accurate cortical surface reconstruction (CSR) is essential for understanding rapid changes in cortical morphometry during early brain development. However, existing CSR methods, designed for adult brain MRI, fall short in reconstructing cortical surfaces from infant MRI, owing to the poor tissue contrasts, partial volume effects, and rapid changes in cortical folding patterns. Here, we introduce an infant-centric CSR method in light of these challenges. Our method, SurfFlow, utilizes three seamlessly connected deformation blocks to sequentially deform an initial template mesh to target cortical surfaces. Remarkably, our method can rapidly reconstruct a high-resolution cortical surface mesh with 360k vertices in approximately one second. Performance evaluation based on an MRI dataset of infants 0 to 12 months of age indicates that SurfFlow significantly reduces geometric errors and substantially improves mesh regularity compared with state-of-the-art deep learning approaches.
X. Chen and J. Zhao—Equal contribution.
This work was supported in part by the United States National Institutes of Health (NIH) through grants MH125479 and EB008374.
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Ahmad, S., et al.: Multifaceted atlases of the human brain in its infancy. Nat. Meth. 20, 55–64 (2023)
Barkovich, M.J., Li, Y., Desikan, R.S., Barkovich, A.J., Xu, D.: Challenges in pediatric neuroimaging. Neuroimage 185, 793–801 (2019)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Cruz, R.S., Lebrat, L., Bourgeat, P., Fookes, C., Fripp, J., Salvado, O.: DeepCSR: a 3D deep learning approach for cortical surface reconstruction. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 806–815 (2021)
Dale, A.M., Fischl, B., Sereno, M.I.: Cortical surface-based analysis: I. segmentation and surface reconstruction. NeuroImage 9(2), 179–194 (1999)
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)
Lebrat, L., et al.: CorticalFlow: a diffeomorphic mesh deformation module for cortical surface reconstruction. arXiv preprint arXiv:2206.02374 (2022)
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
Makropoulos, A., et al.: The developing human connectome project: a minimal processing pipeline for neonatal cortical surface reconstruction. Neuroimage 173, 88–112 (2018)
Santa Cruz, R., et al.: CorticalFlow++: boosting cortical surface reconstruction accuracy, regularity, and interoperability. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13435, pp. 496–505. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16443-9_48
Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: the missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016)
Wang, L., Wu, Z., Chen, L., Sun, Y., Lin, W., Li, G.: iBEAT v2.0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction. Nat. Protoc. 18, 1488–1509 (2023)
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
Zöllei, L., Iglesias, J.E., Ou, Y., Grant, P.E., Fischl, B.: Infant FreeSurfer: an automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0–2 years. Neuroimage 218, 116946 (2020)
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Chen, X., Zhao, J., Liu, S., Ahmad, S., Yap, PT. (2023). SurfFlow: A Flow-Based Approach for Rapid and Accurate Cortical Surface Reconstruction from Infant Brain MRI. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14227. Springer, Cham. https://doi.org/10.1007/978-3-031-43993-3_37
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