MRI-based surface reconstruction and cortical thickness estimation of the human brain: Benchmarking deep-learning based morphometry tools
Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
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.