Abstract
We present a 3-step registration pipeline for differently stained histological serial sections
that consists of 1) a robust pre-alignment, 2) a parametric registration computed on coarse
resolution images, and 3) an accurate nonlinear registration. In all three steps the NGF
distance measure is minimized with respect to an increasingly flexible transformation. We
apply the method in the ANHIR image registration challenge and evaluate its performance
on the training data. The presented method is robust (error reduction in 99.6% of the cases),
fast (runtime 4 seconds) and accurate (median relative target registration error 0.19%).
that consists of 1) a robust pre-alignment, 2) a parametric registration computed on coarse
resolution images, and 3) an accurate nonlinear registration. In all three steps the NGF
distance measure is minimized with respect to an increasingly flexible transformation. We
apply the method in the ANHIR image registration challenge and evaluate its performance
on the training data. The presented method is robust (error reduction in 99.6% of the cases),
fast (runtime 4 seconds) and accurate (median relative target registration error 0.19%).
| Original language | Undefined/Unknown |
|---|---|
| Journal | arXiv preprint arXiv:1903.12063 |
| Publication status | Published - 2019 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
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