Abstract
Traditional cortical surface reconstruction is time consuming and limited by the resolution of brain Magnetic Resonance Imaging (MRI). In this work, we introduce Pial Neural Network (PialNN), a 3D deep learning framework for pial surface reconstruction. PialNN is trained end-to-end to deform an initial white matter surface to a target pial surface by a sequence of learned deformation blocks. A local convolutional operation is incorporated in each block to capture the multi-scale MRI information of each vertex and its neighborhood. This is fast and memory-efficient, which allows reconstructing a pial surface mesh with 150k vertices in one second. The performance is evaluated on the Human Connectome Project (HCP) dataset including T1-weighted MRI scans of 300 subjects. The experimental results demonstrate that PialNN reduces the geometric error of the predicted pial surface by \(30\%\) compared to state-of-the-art deep learning approaches. The codes are publicly available at https://github.com/m-qiang/PialNN.
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This work was supported by the President’s PhD Scholarships at Imperial College London.
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Ma, Q., Robinson, E.C., Kainz, B., Rueckert, D., Alansary, A. (2021). PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction. In: Abdulkadir, A., et al. Machine Learning in Clinical Neuroimaging. MLCN 2021. Lecture Notes in Computer Science(), vol 13001. Springer, Cham. https://doi.org/10.1007/978-3-030-87586-2_8
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