Whole Slide Image Registration for the Study of Tumor Heterogeneity

  • Leslie Solorzano
  • Gabriela M. Almeida
  • Bárbara Mesquita
  • Diana Martins
  • Carla Oliveira
  • Carolina Wählby
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11039)


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 characteristics of the tumor microenvironment can be observed and quantified.


Whole slide image Registration Digital pathology 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Center for Image AnalysisUppsala UniversityUppsalaSweden
  2. 2.i3S, Instituto de Investigação e Inovação em SaúdeUniversidade do PortoPortoPortugal
  3. 3.Ipatimup, Institute of Molecular Pathology and ImmunologyUniversity of PortoPortoPortugal
  4. 4.Faculty of Medicine of the University of PortoPortoPortugal

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