Whole slide image registration for the study of tumor heterogeneity
This work addresses the need for high-accuracy image registration in pathology to quantify intra-tumor heterogeneity and tumor microenvironment characteristics, representing an incremental improvement in domain-specific tools.
The paper tackled the problem of registering gigapixel whole slide images (WSI) to study tumor heterogeneity, addressing challenges like large image size and artifacts from thin sections, and introduced a method using natural sub-regions and a Registration Confidence Map (RCM) to enable accurate comparison of protein patterns.
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.