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. Author manuscript; available in PMC: 2022 Dec 30.
Published in final edited form as: Neuroimage. 2022 Feb 3;251:118933. doi: 10.1016/j.neuroimage.2022.118933

Fig. 12.

Fig. 12.

Flexible-versus fixed-resolution networks. FastSurferVINN (orange) is comparable to, or outperforms, all fixed-resolution networks (green) (left plot: 0.8 mm from RS only, right plots: 1.0 mm) with respect to the Dice Similarity Coefficient (DSC, top) and average surface distance (ASD, bottom). On the submillimeter scans (left plots) generalization to an unseen dataset (HCPL) is significantly improved. Results are consistently better for the 1.0 mm scans (right plot). To highlight cumulative VINN and architectural optimizations, we also compare with the state-of-the-art FastSurferCNN (gray, without optimizations from Sections 4.1 and 4.2, which are already included in CNN*). We retrain this 1 mm fixed-resolution network ensuring equal training datasets and conditions.