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Whole Slide Image Registration for the Study of Tumor Heterogeneity
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.ORCID iD: 0000-0001-8658-6417
Ipatimup, Institute of Molecular Pathology and ImmunologyUniversity of Porto. (i3S Instituto de Investigação e Inovação em Saúde Universidade do Porto Portugal)
(i3S Instituto de Investigação e Inovação em Saúde Universidade do Porto Portugal)
(i3S Instituto de Investigação e Inovação em Saúde Universidade do Porto Portugal)
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2018 (English)In: MICCAI 2018 - International Workshop on Ophthalmic Medical Image Analysis: OMIA 2018, COMPAY 2018: Computational Pathology and Ophthalmic Medical Image Analysis, Spain: Springer, 2018, p. 95-102Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Spain: Springer, 2018. p. 95-102
Series
Lecture Notes in Computer Science (LNCS) ; 11039
Keywords [en]
WSI, digital pathology
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-368006DOI: 10.1007/978-3-030-00949-6_12ISBN: 978-3-030-00949-6 (electronic)OAI: oai:DiVA.org:uu-368006DiVA, id: diva2:1267389
Conference
MICCAI 2018
Projects
TissUUmapsAvailable from: 2018-12-02 Created: 2018-12-02 Last updated: 2018-12-04

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Publisher's full texthttps://doi.org/10.1007/978-3-030-00949-6_12

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Solorzano, LeslieWählby, Carolina

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Solorzano, LeslieOliveira, CarlaWählby, Carolina
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Computerized Image Analysis and Human-Computer InteractionScience for Life Laboratory, SciLifeLabDivision of Visual Information and Interaction
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