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This research aims to evaluate the efficiency and reliability of deep learning models for brain MRI surface reconstruction.

June 15, 2026Scientific ReportsOpen Access

Fast surface reconstruction of human brain MRI: benchmarking deep-learning based morphometry tools

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This research aims to evaluate the efficiency and reliability of deep learning models for brain MRI surface reconstruction.
Evaluated three deep learning models: DeepSCAN, FastSurferCNN, and QuickNAT.
Adapted a surface reconstruction pipeline from the FreeSurfer software.
Assessed performance using human MRI datasets and a synthetic dataset with known reference metrics.
The DeepSCAN-based pipeline showed the highest agreement with FreeSurfer metrics in human data.
Demonstrated the greatest fidelity to expected metrics in the synthetic dataset.
Identified as a fast and reliable alternative for high-throughput structural MRI processing.

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Randomized trial benchmarks deep learning tools for fast brain MRI processing, suggesting potential for clinical applications.

Mello et al. (2026) studied this question.

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