# Whole slide image registration for the study of tumor heterogeneity

## TL;DR

This paper presents a novel method for high-accuracy registration of gigapixel whole slide images, enabling detailed study of tumor heterogeneity and microenvironment by addressing challenges of large size and artifacts.

## Contribution

It introduces a new approach combining automatic and manual feature selection on natural sub-regions, along with a visualization tool called Registration Confidence Map.

## Key findings

- Effective registration of large gigapixel images achieved
- Interactive sub-region selection improves relevance of analysis
- Registration Confidence Map visualizes local registration quality

## Abstract

Consecutive thin sections of tissue samples make it possible to study local variation in e.g. protein expression and tumor heterogeneity by staining for a new protein in each section. In order to compare and correlate patterns of different proteins, the images have to be registered with high accuracy. The problem we want to solve is registration of gigapixel whole slide images (WSI). This presents 3 challenges: (i) Images are very large; (ii) Thin sections result in artifacts that make global affine registration prone to very large local errors; (iii) Local affine registration is required to preserve correct tissue morphology (local size, shape and texture). In our approach we compare WSI registration based on automatic and manual feature selection on either the full image or natural sub-regions (as opposed to square tiles). Working with natural sub-regions, in an interactive tool makes it possible to exclude regions containing scientifically irrelevant information. We also present a new way to visualize local registration quality by a Registration Confidence Map (RCM). With this method, intra-tumor heterogeneity and charateristics of the tumor microenvironment can be observed and quantified.