Robust, fast and accurate: a 3-step method for automatic histological image registration

Johannes Lotz; Nick Weiss; Stefan Heldmann

Robust, fast and accurate: a 3-step method for automatic histological image registration

Johannes Lotz, Nick Weiss, Stefan Heldmann

Institut für Mathematische Methoden der Bildverarbeitung

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%).

Originalsprache	undefiniert/unbekannt
Zeitschrift	arXiv preprint arXiv:1903.12063
Publikationsstatus	Veröffentlicht - 2019

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Weitere Links

https://arxiv.org/abs/1903.12063

Zitieren

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title = "Robust, fast and accurate: a 3-step method for automatic histological image registration",

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\%). ",

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AB - 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%).

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