Comparison of Consecutive and Re-stained Sections for Image Registration in Histopathology

Purpose: In digital histopathology, virtual multi-staining is important for diagnosis and biomarker research. Additionally, it provides accurate ground-truth for various deep-learning tasks. Virtual multi-staining can be obtained using different stains for consecutive sections or by re-staining the same section. Both approaches require image registration to compensate tissue deformations, but little attention has been devoted to comparing their accuracy. Approach: We compare variational image registration of consecutive and re-stained sections and analyze the effect of the image resolution which influences accuracy and required computational resources. We present a new hybrid dataset of re-stained and consecutive sections (HyReCo, 81 slide pairs, approx. 3000 landmarks) that we made publicly available and compare its image registration results to the automatic non-rigid histological image registration (ANHIR) challenge data (230 consecutive slide pairs). Results: We obtain a median landmark error after registration of 7.1 mm (HyReCo) and 16.0 mm (ANHIR) between consecutive sections. Between re-stained sections, the median registration error is 2.3 mm and 0.9 mm in the two subsets of the HyReCo dataset. We observe that deformable registration leads to lower landmark errors than affine registration in both cases, though the effect is smaller in re-stained sections. Conclusion: Deformable registration of consecutive and re-stained sections is a valuable tool for the joint analysis of different stains. Significance: While the registration of re-stained sections allows nucleus-level alignment which allows for a direct analysis of interacting biomarkers, consecutive sections only allow the transfer of region-level annotations. The latter can be achieved at low computational cost using coarser image resolutions.