  Cortical surface reconstruction plays a fundamental role in modeling the
rapid brain development during the perinatal period. In this work, we propose
Conditional Temporal Attention Network (CoTAN), a fast end-to-end framework for
diffeomorphic neonatal cortical surface reconstruction. CoTAN predicts
multi-resolution stationary velocity fields (SVF) from neonatal brain magnetic
resonance images (MRI). Instead of integrating multiple SVFs, CoTAN introduces
attention mechanisms to learn a conditional time-varying velocity field (CTVF)
by computing the weighted sum of all SVFs at each integration step. The
importance of each SVF, which is estimated by learned attention maps, is
conditioned on the age of the neonates and varies with the time step of
integration. The proposed CTVF defines a diffeomorphic surface deformation,
which reduces mesh self-intersection errors effectively. It only requires 0.21
seconds to deform an initial template mesh to cortical white matter and pial
surfaces for each brain hemisphere. CoTAN is validated on the Developing Human
Connectome Project (dHCP) dataset with 877 3D brain MR images acquired from
preterm and term born neonates. Compared to state-of-the-art baselines, CoTAN
achieves superior performance with only 0.12mm geometric error and 0.07%
self-intersecting faces. The visualization of our attention maps illustrates
that CoTAN indeed learns coarse-to-fine surface deformations automatically
without intermediate supervision.
