We develop algorithms and methods that enable the quantification of a broad range of phenotypical alterations, the precise localization of signalling events, and the ability to correlate such events in the context of biological specimens.
This work can be structured into the following three major areas:
- Analysis of shape, structure, and spatial context,
- Function and dynamic biological processes,
- Enablement of new imaging methods
Being affiliated to the Ludwig Institute of Cancer Research, we have the opportunity to develop these methods in the context of concrete biological questions.
1. Clinical management and diagnosis
Barrett’s oesophagus is a chronic inflammatory condition that confers an increased risk of oesophageal adenocarcinoma. Currently, endoscopic examinations and quadratic non-targeted biopsies (taken in the affected section of the oesophagus) are recommended for the surveillance of Barrett’s oesophagus. We aim to develop computer-assisted methods that will improve the diagnosis and monitoring of Barrett’s oesophagus by enabling the collection of more targeted biopsies, facilitating a quantitative assessment of histology and eventually providing the basis for linking endoscopy imaging with pathology.
Figure. We have developed deep learning architecture for detecting glandular structures (left) and specific cell types (epithelial cells and lymphocytes) (right) in oesophageal tissue. These results illustrate the first steps we have taken toward developing the concept of a tissue map for histology imaging.
2. Evaluation of new treatments
Recently, cancer immunotherapy has yielded impressive breakthroughs. However, effective clinical management requires methods for predicting therapeutic response, which is linked to the presence of immune cell infiltration in tumours. We propose a systematic approach for quantifying inflammatory changes in the example of oesophageal adenocarcinoma. We are developing computational pathology tools to monitor changes in the cellular population and tissue architecture for predicting response to therapy.
3. Understanding molecular mechanisms
The relevance of in vitro studies hinges on the value of cell-based model systems for predicting what occurs in vivo. It is therefore critical that these systems phenotypically represent their in vivo counterparts. In contrast to traditional monolayers, 3D cultures can restore specific biochemical and morphological features that are similar to the corresponding tissue. Quantitative imaging methods will enable the analysis of cell-to-cell and cell-to-matrix interactions that characterize the microenvironment as well as migration and invasion mechanisms. A more ambitious goal is the analysis of collective cell migration, which plays a crucial role in development and disease progression.