Abstract
We present cortical surface parcellation using spherical deep convolutional neural networks. Traditional multi-atlas cortical surface parcellation requires inter-subject surface registration using geometric features with slow processing speed on a single subject (2–3 h). Moreover, even optimal surface registration does not necessarily produce optimal cortical parcellation as parcel boundaries are not fully matched to the geometric features. In this context, a choice of training features is important for accurate cortical parcellation. To utilize the networks efficiently, we propose cortical parcellation-specific input data from an irregular and complicated structure of cortical surfaces. To this end, we align ground-truth cortical parcel boundaries and use their resulting deformation fields to generate new pairs of deformed geometric features and parcellation maps. To extend the capability of the networks, we then smoothly morph cortical geometric features and parcellation maps using the intermediate deformation fields. We validate our method on 427 adult brains for 49 labels. The experimental results show that our method outperforms traditional multi-atlas and naive spherical U-Net approaches, while achieving full cortical parcellation in less than a minute.
P. Parvathaneni and S. Bao contributed equally to this work.
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Notes
- 1.
The code is available at https://github.com/ilwoolyu/HSD.
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Acknowledgments
This work was supported in part by the National Institutes of Health under Grants R01EB017230, R01MH102266, R01NS097783, R01MH102272, and R01MH098098, in part by the National Science Foundation under Grant CAREER IIS 1452485, in part by the VISE/VICTR under Grant VR3029, and in part by NVIDIA Corporation under GPU Grant Program.
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Parvathaneni, P. et al. (2019). Cortical Surface Parcellation Using Spherical Convolutional Neural Networks. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham. https://doi.org/10.1007/978-3-030-32248-9_56
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