Department of Information Technology

Medical Image Processing

Three-dimensional (3D) imaging techniques, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), are routinely used in medicine to generate high-resolution volume images of the human body. In addition to qualitative analysis performed by radiologists, digital image analysis can be used to extract quantitative information about the patient based on the image data. The ability to quickly and accurately extract quantitative information from medical images has a huge potential in clinical research and, ultimately, in everyday clinical radiological practice.
In the medical image processing (MedIP) group, we develop interactive methods and methods for large-scale analysis in medical imaging.

Interactive deep learning segmentation for decision support in neuroradiology

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.

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Interactive Segmentation and Analysis of Medical Images

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Three-dimensional (3D) imaging technique such as computed tomography (CT) and magnetic resonance imaging (MRI) are now routinely used in medicine. This has lead to an ever increasing flow of high-resolution, high-dimensional, image data that needs to be qualitatively and quantitatively analyzed. Typically, this analysis requires accurate segmentation of the image.
At CBA, we have been developing powerful new methods for interactive image segmentation. In this project, we seek to employ these methods for segmentation of medical images, in collaboration with the Dept.~of Surgical Sciences at the Uppsala University Hospital.
A publicly available software for interactive segmentation, emph{SmartPaint}, can be downloaded from url{http://www.cb.uu.se/~filip/SmartPaint/}. To date, this software has been downloaded more than 1100 times.

Image Processing for Virtual Design of Surgical Guides and Plates

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.

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Virtual surgical planning for soft tissue resection and reconstruction

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With the increasing use of 3D models and CAD technologies in the medical domain, virtual surgical planning is now frequently used. Most of current solutions focus on bone surgical operations. However, for head and neck oncologic resection, soft tissue ablation and reconstruction are common operations. By removing the tumor, a defect in the face is created consisting of different tissue layers. To reconstruct this defect it is usually needed to transplant vascularized tissue from other parts of the body. In collaboration with the Dept. of Surgical Science at the UU Hospital, we aim at providing a virtual planning solution for such surgical operations. We developed a new method to estimate the shape and dimensions of soft tissue resections. Our approach takes advantage of a simple sketch-based interface, which allows the user to paint the contour of the resection on a patient specific 3D model reconstructed from a CT scan. The volume is then virtually cut and carved following this pattern to provide a 3D model of the resected volume. We then seek to develop a numerical model, based on finite element method, to simulate the non-rigid behavior of the soft tissue flap during the reconstruction process.

Imiomics - Large-Scale Analysis of Medical Volume Images

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.

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Updated  2019-05-13 14:02:12 by Elisabeth Wetzer.