Project MIRA

This project is devoted to redefine the state-of-the-art in medical image analysis by developing a new generation of machine intelligence using powerful techniques of representation learning.
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AI & Machine Learning for Semantic Imaging

We employ cutting edge machine learning and artificial intelligence techniques for extracting clinically useful information from raw medical image data. This is what we call Semantic Imaging. Applications include cardiac imaging, whole-body oncology and brain lesion analysis.
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Automatic Quality Control

In clinical practice, it is of utmost importance to be able to detect when an automatic method fails in order to avoid inclusion of incorrect information into clinical decisions. In this project we develop methods for quality control and performance prediction of automated image analysis tools.
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Medical Computer Vision & Augmented Reality

We bring together advanced computer vision methods such as dense 3D real-time reconstruction using depth cameras and machine learning based image analysis for applications in medical augmented reality.
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Spine Image Analysis

The focus in this project is on quantitative spinal imaging and image-based analysis of spinal pathologies. In this context, we are developing algorithms for vertebra localization, longitudinal image registration, and whole spine segmentation.
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Image Registration & Segmentation Methods

We are continuously working on the development of new algorithms for automatic registration and segmentation of multi-modal medical image data. Many of those methods are for general purposes and can be employed in various applications.
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