Template-Based Cortical Surface Reconstruction with Minimal Energy Deformation
This work addresses a specific bottleneck in neuroimaging for researchers, but it is incremental as it builds on an existing model with a new regularization technique.
The paper tackled the challenge of ensuring optimal and consistent deformations in learning-based cortical surface reconstruction by introducing a Minimal Energy Deformation loss as a regularizer, resulting in improved training consistency and reproducibility without compromising accuracy or topological correctness.
Cortical surface reconstruction (CSR) from magnetic resonance imaging (MRI) is fundamental to neuroimage analysis, enabling morphological studies of the cerebral cortex and functional brain mapping. Recent advances in learning-based CSR have dramatically accelerated processing, allowing for reconstructions through the deformation of anatomical templates within seconds. However, ensuring the learned deformations are optimal in terms of deformation energy and consistent across training runs remains a particular challenge. In this work, we design a Minimal Energy Deformation (MED) loss, acting as a regularizer on the deformation trajectories and complementing the widely used Chamfer distance in CSR. We incorporate it into the recent V2C-Flow model and demonstrate considerable improvements in previously neglected training consistency and reproducibility without harming reconstruction accuracy and topological correctness.