Professor of Engineering Science
The aim of his research is to enhance our understanding of complex biological processes through the analysis of image data that has been acquired at the microscopic scale. Jens Rittscher develops algorithms and methods that enable the quantification of a broad range of phenotypical alterations, the precise localisation of signalling events, and the ability to correlate such events in the context of the biological specimen. 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
This algorithm development needs to be guided by a firm understanding of the broader application context which is indicated in the figure below. Sophisticated algorithms are now necessary to image increasingly complex model systems over an extended period of time. In order to understand the role of certain genetic modifiers we need to relate these to the image derived measurements and features.
Dr. Jens Rittscher has been appointed as a University Research Lecturer in 2013 and he is the first joint academic appointment between the Institute of Biomedical Engineering and the Nuffield Department of Medicine. In particular his work supports the Target Discovery Institute and Ludwig Institute of Cancer Research. In addition to his research in the field of biomedical imaging, Jens Rittscher has worked extensively in the area of video surveillance, the automatic annotation of video, and understanding of volumetric seismic data.
Before coming to Oxford in 2013 Jens Rittscher led the Computer Vision Laboratory at GE Global Research in Niskayuna, NY, USA. He joined GE in 2001 after completing his PhD at the Department of Engineering Science at University of Oxford. During this time he was part of the Visual Dynamics Group led by Andrew Blake. He received his Diploma in Mathematics and Computer Science from the University of Bonn, Germany. Jens Rittscher held a position as an adjunct assistant professor at the Rensselaer Polytechnic Institute. He is a member of IEEE and acts as an elected member of the IEEE SPS Technical Committee on Bio Image and Signal Processing.
Please visit the IBME page for additional details.
Quantitative interpretation of bone marrow biopsies in MPN-What's the point in a molecular age?
Ryou H. et al, (2023), British journal of haematology
Beyond attention: deriving biologically interpretable insights from
weakly-supervised multiple-instance learning models
Bonnaffé W. et al, (2023)
SSL-CPCD: Self-supervised learning with composite pretext-class
discrimination for improved generalisability in endoscopic image analysis
Xu Z. et al, (2023)
Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19·67 million people and evaluation of model performance against seven other risk prediction models.
Liao W. et al, (2023), Lancet Respir Med
A multi-centre polyp detection and segmentation dataset for generalisability assessment.
Ali S. et al, (2023), Sci Data, 10