Medical Image Processing

Many brain diseases can damage brain cells (nerve cells), which can lead to loss of nerve cells and, secondarily, loss of brain volume. Technical imaging advancements allow detection and quantification of very small tissue volumes in magnetic resonance (MR) neuroimaging. Due to the enormous amount of information in a typical MR brain volume scan, and difficulties such as partial volume effects, noise, artefacts, etc., interactive tools for computer aided analysis are absolutely essential for this task.
Available interactive methods are often not suited for this problem. Deep learning by convolution neural networks has the ability to learn complex structures from training data. However, deep learning is often too slow for interactive processing.
We develop, analyze and evaluate interactive deep learning segmentation methods for quantification and treatment response analysis in neuroimaging. Interaction speed is obtained by dividing the segmentation procedure into an offline pre-segmentation step and an on-line interactive loop in which the user adds constraints until satisfactory result is obtained.
The overarching aim is to allow detailed correct diagnosis, as well as accurate and precise analysis of treatment response in neuroimaging, in particular in quantification of intracranial aneurysm remnants and brain tumors (Gliomas WHO-grades III and IV) growth.

An important part of virtual planning for reconstructive surgery, such as cranio-maxillofacial (CMF) surgery, is the design of customized surgical tools and implants. In this project, we are looking into how distance transforms and constructive solid geometry can be used to generate 3D printable models of surgical guides and plates from segmented computed tomography (CT) images of a patient, and how the accuracy and precision of the modelling can be improved by using grayscale image information in combination with anti-aliased distance transforms. Another part of the project is to develop simple and interactive tools that allow a surgeon to create the models. We have implemented a set of design tools in our existing surgery planning system, HASP, and are currently testing them with surgeons.

In this project, we mainly process magnetic resonance tomography (MR) images. MR images are very useful in clinical use and in medical research, e.g., for analyzing the composition of the human body. At the division of Radiology, UU, a huge amount of MR data, including whole body MR images, is acquired for research on the connection between the composition of the human body and disease.
To compare volume images voxel by voxel, we develop a large scale analysis method, which is enabled by image registration methods. These methods utilize, for example, segmented tissue and anatomical landmarks. Based on this idea, we have developed Imiomics (imaging omics) -- an image analysis concept, including image registration, that allows statistical and holistic analysis of whole-body image data. The Imiomics concept is holistic in three respects: (i) The whole body is analyzed, (ii) All collected image data is used in the analysis and (iii) It allows integration of all other collected non-imaging patient information in the analysis.