Research Statement and Vision

The Computational Pathology Group try to understand the correlation between cell morphology, function and the underlaying molecular features. To link different features to morphology, deep learning and quantitative image analysis are used.
We want to closely connect our research to clinics and aim to improve decision making during diagnostics. For that we aim to develop tools which support medical professionals and provide fast information about additional features of a tumor based on the routinely generated H&E slices.


From tissue morphology to molecular subclasses in gastric cancer

In Gastric cancer four molecular subclasses are defined (CIN, EBV, GS and MSI) which should be predicted based on morphological features extracted from H&E stained tissue slices using convolutional neuronal networks (cNN’s). Here we also focus an tumor heterogeneity, its influence onto the training process and we will use multiplex imaging for further validation.

Cell morphology in lymphomas

We investigate different types of lymphomas (cHL and ALCL), which share some molecular properties (e.g. positive for CD30) but have a different clinical course. We quantify cell morphological structures used for cell migration, quantify cell movement and use cNN’s to characterize the migration type to collect information which could help to understand the early spreading of ALCL. Publication: Goncharova*, Flinner* et al. 2019; PMID: 31581676

Intra cellular morphological changes during cell differentiation in cyanobacteria

The filamentous cyanobacterium Anabaena sp. PCC7120 is abele to differentiate single cells into heterocysts in case of nitrogen deficiency. Morphological features should be extracted from EM images (acquired at different timepoints of the differentiation process) and are correlated to the respective gene expression profile in order to identify the genes which are involved in the morphological changes using different techniques like e.g. Person, Spearman or MIC/MINE. At the end t