We compare variational image registration in consectutive and re-stained sections from histopathology. We present a fully-automatic algorithm for non-parametric (nonlinear) image registration and apply it to a previously existing dataset from the ANHIR challenge (230 slide pairs, consecutive sections) and a new dataset (hybrid re-stained and consecutive, 81 slide pairs, ca. 3000 landmarks) which is made publicly available. Registration hyperparameters are obtained in the ANHIR dataset and applied to the new dataset without modification. In the new dataset, landmark errors after registration range from 13.2 micrometers for consecutive sections to 1 micrometer for re-stained sections. We observe that non-parametric registration leads to lower landmark errors in both cases, even though the effect is smaller in re-stained sections. The nucleus-level alignment after non-parametric registration of re-stained sections provides a valuable tool to generate automatic ground-truth for machine learning applications in histopathology.
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