arXiv Machine Learning September 14, 2026 By Saeb Tounsi, Jo\"el Chavas, Pietro Gori, Vincent Frouin, Denis Rivi\`ere, Jean-Fran\c{c}ois Mangin

Geometric-to-Semantic Spherical Transfer Learning for Cortical Sulci Labeling

Read the original on arXiv Machine Learning →

The paper introduces a Geometric-to-Semantic Spherical Transfer Learning framework for labeling cortical sulci on brain surfaces. It first pre‑trains a spherical encoder on ~30,000 unlabeled UK Biobank subjects using only curvature and depth, then injects sulcal fundi lines as a soft‑initialized Topological Prior Injector to bridge the geometric‑semantic gap. Experiments show the method surpasses fully supervised baselines, achieving a mean Dice score of 0.77 and delivering the largest gains on variable and tertiary sulci.

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