Projects at BMDLab


  • CLARIFY:  Cloud artificial intelligence for Pathology.  BMDLab are a part of the Eurpoean Marie Sklodowska Curie ITN network  CLARIFY.  BMDLab is the benificiary of two ECR - Early stage researchers of this project, starting in the spring 2020.  
  • CPR and Smartphone: Out-of-hospital cardiac arrest (OHCA) is a major cause of mortality throughout many regions of the world.  Calling an emergency number should be the first thing the bystander does.  Today almost everybody have a smart phone, permitting the incorporation other functions in addition to speech by using an emergency app.  In case of emergency, the app is activated and takes control over the phone.   A dispatcher receives the phone call, and in addition to talking to the bystander, the app provides the dispatcher with GPS coordinates.  At UiS we are working on including chest compression measurement using the video camera of the smart phone placed beside the patient during bystander CPR.  Image processing is performed on the smart phone, and the dispatcher will be provided with the detected compression rate, if any. 
  • Histological image processing:  In this project we look at region of interest extraction and segmentation in  histological images, as well as diagnostic classification and prognostic prediction.  Digitally scanned Whole slide images (WSI) are provided from the Department of Pathology, Stavanger University Hospital (SUS).  Currently the project is concentrated on urothelial carcinoma, i.e. urinary bladder cancer, where a a large dataset of patients with follow-up data on different prognoses and further development is available. We are investigating deep convolutional neural networks (CNN) and data driven learning as well as more classical handcrafted feature extraction methods.     
  • Newborn Data Analysis: We have been working together with Stavanger University Hospital (SUS) and Laerdal Global Health and more partners in the SAFER BIRTHS study, since 2013 to analyze data from stabilization and resuscitation of newborns using signals from NeoBeat and from ventilation.  In addition we are working with fetal heart rate signals collected using Moyo.   The research on Moyo signals have revcieved funding from Idella, starting spring 2020.   Newborn death is a major problem in the third world, and the hypothesis is that using the child's heart rate during and after childbirth one will be better able to make the right decisions that will increase the child's chance to survive. This is part of a larger project where equipment for measuring important data that documents the treatment and the child's response to this has been developed. We are already well in the process of developing methods to effectively analyze the interaction between treatment and response in the child.  
  • Analysis of magnetic resonance imaging (MR) and intracardial ECG from patients with myocardial infarction: Some patients who have had myocardial infarction will be at increased risk of arrhythmias and acute cardiac arrest. In this regard, it is interesting to study changes in the heart's ability to direct electrical impulses, as such changes can lead to the development of dangerous arrhythmias. We study MRI images of the heart from patients with myocardial infarction. We have subprojects that engage in automatic segmentation of cardiac muscle from surrounding tissues, and automatic segmentation of scar tissue from healthy tissues from MR images of the heart. Since there are hypotheses that there are boundaries between healthy and dead tissues that induce arrhythmias, we are working to quantify the degree of injury as a probability mapping of the myocardium where the likelihood scar is visualized.
  • NEEDED: The world's largest health survey of expected healthy individuals participating in a training contest was conducted during the "Nordsjørittet" in 2014. Blood tests, blood pressure and ECG 24h before - as well as 3h and 24h were taken after the ride from more than 1,000 participants examined on all three times. In addition, there are complete data from pulse watches from approx. 300 of these. The purpose of this project is, by means of signal processing, to develop new methods for analyzing these measurement data. The goal is to develop methods that should help to indicate the risk of heart disease and health benefits of physical activity
  • Analysis of acute medical data: In Norway there are approximately 3000 annual cases of patients experiencing cardiac arrest outside hospitals in addition to a significant number in hospitals. Ongoing research is about developing new signal processing algorithms and statistical models to interpret, predict and model the effect that the treatment given by acute cardiac arrest has on the patient. Various parts of this research are conducted in collaboration with several research groups, both nationally and internationally.
  • Analysis of brain magnetic resonance (MR) images from patients with dementia: The purpose of the project is to investigate whether it is possible to use imaging techniques to diagnose dementia patients from the MR images. Today, a combination of markers is used to determine diagnosis. In the project we look at hyperintense areas in the white substance part of the brain. These hyperintense areas are known to increase in size also in normal, as a function of age. However, the size and location are also linked to different forms of dementia.
  •  Analysis of perfusion CT (CTP) images for acute stroke patients
© Kjersti Engan 2018