MRI-based surface reconstruction and cortical thickness estimation of the human brain: Benchmarking deep-learning based morphometry tools

Establishing reliable and time efficient pipelines for structural MRI segmentation, parcellation and surface reconstruction, is essential to explore the potential clinical applications of research-grade morphometry tools. The integration between deep-learning based methods for fast whole-brain segmentation and the well known surface reconstruction algorithms is a viable alternative to perform this task. In this work, we applied this idea with three deep-learning based cortical parcellation models, DeepSCAN, FastsurferCNN and QuickNAT. With a 11 min surface reconstruction pipeline, we evaluated the performance of each segmentation beyond the voxel-based approaches and dice coefficient comparison between the generated parcellation and Freesurfer’s established silver standard. To prove the concept, we performed a direct comparison between the morphological variables obtained by our methodology and Freesurfer. Using a synthetic dataset, we benchmark each reconstruction pipeline based on the similarity to the ground-truth surface and reproduction of the expected surface-based metrics. The most robust pipeline across the human dataset and closer to the synthetic ground truth was based on DeepSCAN segmentation, producing a reliable morphometric tool with a processing time realistic for clinical applications like diagnostics support in individuals.