Nuclei-Location Based Point Set Registration of Multi-Stained Whole Slide Images

TL;DR

This paper introduces a nuclei-location based point set registration method for aligning multi-stained Whole Slide Images, addressing complex tissue deformations at the nuclei level to improve downstream cellular analysis.

Contribution

It presents a novel nuclei-based non-rigid registration approach that outperforms existing methods in aligning multi-stained WSIs at the nuclei level.

Findings

Outperforms established registration algorithms on nuclei-level alignment

Effective on multi-stained WSIs with complex tissue deformations

Applicable to various staining techniques with nuclei detection

Abstract

Whole Slide Images (WSIs) provide exceptional detail for studying tissue architecture at the cell level. To study tumour microenvironment (TME) with the context of various protein biomarkers and cell sub-types, analysis and registration of features using multi-stained WSIs is often required. Multi-stained WSI pairs normally suffer from rigid and non-rigid deformities in addition to slide artefacts and control tissue which present challenges at precise registration. Traditional registration methods mainly focus on global rigid/non-rigid registration but struggle with aligning slides with complex tissue deformations at the nuclei level. However, nuclei level non-rigid registration is essential for downstream tasks such as cell sub-type analysis in the context of protein biomarker signatures. This paper focuses on local level non-rigid registration using a nuclei-location based point set registration approach for aligning multi-stained WSIs. We exploit the spatial distribution of nuclei that is prominent and consistent (to a large level) across different stains to establish a spatial correspondence. We evaluate our approach using the HYRECO dataset consisting of 54 re-stained images of H\&E and PHH3 image pairs. The approach can be extended to other IHC and IF stained WSIs considering a good nuclei detection algorithm is accessible. The performance of the model is tested against established registration algorithms and is shown to outperform the model for nuclei level registration.