GED: Gaussian Expected Deformation for Mask-Free Damaged-Section Registration

Folds and tears in serial-section electron microscopy create abrupt motion changes between neighboring tissue regions. We present Gaussian Expected Deformation (GED), a pairwise registration method that learns the spatial influence of local displacement fields without damage masks. Anisotropic Gaussian supports can extend along damage boundaries and narrow across them, reducing the mixing of incompatible motions. Sequential RANSAC extracts multiple motion groups from feature correspondences to initialize the fields. Per-pair optimization then refines their parameters and supports through image-similarity and correspondence objectives. On the synthetic benchmark, GED reduces valid endpoint error from 2.09 to 0.215 pixels relative to GPO, a reduction of 89.7%. Near-damage error falls from 6.66 to 1.10 pixels, a reduction of 83.4%. Controlled support ablations and learned-weight visualizations link anisotropy to lower near-damage error and cross-boundary mixing.