A registration error estimation framework for correlative imaging

TL;DR

This paper introduces a multivariate linear regression framework for estimating registration errors in correlative imaging, aiding biologists in analyzing multimodal images with improved accuracy and decision support.

Contribution

It presents a novel application of multivariate linear regression for registration error estimation in point-based correlative imaging, including rigid and affine transformations with anisotropic noise.

Findings

Framework effectively estimates registration errors in various transformations.

Open-source Ec-CLEM plugin implements the proposed method.

Supports decision-making in multimodal bioimaging workflows.

Abstract

Correlative imaging workflows are now widely used in bioimaging and aims to image the same sample using at least two different and complementary imaging modalities. Part of the workflow relies on finding the transformation linking a source image to a target image. We are specifically interested in the estimation of registration error in point-based registration. We propose an application of multivariate linear regression to solve the registration problem allowing us to propose a framework for the estimation of the associated error in the case of rigid and affine transformations and with anisotropic noise. These developments can be used as a decision-support tool for the biologist to analyze multimodal correlative images and are available under Ec-CLEM, an open-source plugin under ICY.