Visual Exploration for Imaging Mass Cytometry

Description: Tissue functionality is determined by the characteristics of tissue-resident cells and their interactions within their microenvironment. Imaging Mass Cytometry offers the opportunity to distinguish cell types with high precision and link them to their spatial location in intact tissues at sub-cellular resolution. This technology produces large amounts of spatially-resolved high-dimensional data, which constitutes a serious challenge for the data analysis. In this project, we explore different ways to visually explore such data. The goal is to provided integrated, interactive workflows, to identify the contained cell types, their spatial location, macrostructures and neighborhoods, etc.

If you are a student looking for a possible internship or thesis topic, we have several open projects and possibilities. Projects can be at LUMC or in collaboration with the Computer Graphics and Visualization group at TU Delft. Please contact Thomas Höllt for further information.

LCBC Team Members

Thomas Höllt
Antonis Somarakis


Partial funding from the Leiden University Data Science Research Programme.

Related Publications

Antonis Somarakis Vincent van Unen,  Frits Koning,  Boudewijn Lelieveldt,  and Thomas Höllt
In IEEE Transactions on Visualization and Computer Graphics, 2019
Na Li,  Vincent van Unen,  Tamim Abdelaal,  Nannan Guo,  Sofya Kasatskaya, et al.
Nature Immunology, 2019
 Cover Image