From Traditional to Deep Learning Approaches in Whole Slide Image Registration: A Methodological Review

TL;DR

This review paper discusses the evolution from traditional to deep learning methods for whole slide image registration in histopathology, highlighting current approaches, challenges, datasets, and future directions.

Contribution

It provides a comprehensive review of existing registration techniques, emphasizing deep learning approaches, and identifies key challenges and future research opportunities.

Findings

Deep learning methods are increasingly used for WSI registration.

Current approaches face challenges like large image size and tissue variability.

Open challenges include dataset availability and method robustness.

Abstract

Whole slide image (WSI) registration is an essential task for analysing the tumour microenvironment (TME) in histopathology. It involves the alignment of spatial information between WSIs of the same section or serial sections of a tissue sample. The tissue sections are usually stained with single or multiple biomarkers before imaging, and the goal is to identify neighbouring nuclei along the Z-axis for creating a 3D image or identifying subclasses of cells in the TME. This task is considerably more challenging compared to radiology image registration, such as magnetic resonance imaging or computed tomography, due to various factors. These include gigapixel size of images, variations in appearance between differently stained tissues, changes in structure and morphology between non-consecutive sections, and the presence of artefacts, tears, and deformations. Currently, there is a noticeable gap in the literature regarding a review of the current approaches and their limitations, as well as the challenges and opportunities they present. We aim to provide a comprehensive understanding of the available approaches and their application for various purposes. Furthermore, we investigate current deep learning methods used for WSI registration, emphasising their diverse methodologies. We examine the available datasets and explore tools and software employed in the field. Finally, we identify open challenges and potential future trends in this area of research.