Research

Interdisciplinarity is what drives modern research...

Research at DeepMI Lab

For AI research see our DeepMI Lab Webpage

Research Domains

AI in Medical Imaging, Deep-Learning, Convolutional Neural Networks,
Medical Image Computing, Computational NeuroImaging, Big Data Analysis,
Machine Learning, Computer Vision and Graphics, Computational Statistics,
Differential and Computational Geometry, Computational Topology,
Human Computer Interaction, Geometric Modeling, Computer-aided Design

Research Directions

In close collaboration with clinical and industrial partners, we develop the next-generation computational techniques for the analysis of large biomedical image datasets, including:

Development of innovative methods for reliable image acquisition, processing, analysis and interpretation – in particular sensitive longitudinal analysis, registration, reconstruction, segmentation and predictive modeling.
Deep- and machine-learning, computer vision and statistical modeling for the extraction of biomarkers and other clinically relevant information from large data sets (big data) – in particular for computer-aided diagnosis (at presymptomatic stages) and prognosis, personalized medicine, treatment planning, patient stratification, and identification of risk- or preserving factors of neurodegenerative disease.
Our research focuses on improving our understanding of brain development, neurodegeneration (aging, dementia, Alzheimer's disease, Huntington's disease), the sensitive quantification of subtle drug effects, and improving tumor treatment assessment, via multi-modal imaging.

Methodology

Image Segmentation