Vacancy: PhD position for development and evaluation of automatic image processing methods to separate arterial and venous trees in chest CT scans for application in pulmonary embolism detection (closed)
The Diagnostic Image Analysis Group at the Department of Radiology, Radboud University Nijmegen Medical Centre, is offering a PhD position.
The Diagnostic Image Analysis Group is a research division of the Department of Radiology of the Radboud University Nijmegen Medical Centre. Nijmegen is the oldest Dutch city with a rich history and one of the liveliest city centers in the Netherlands. Radboud University has over 17,000 students. Radboud University Nijmegen Medical Centre (RUNMC) is a leading academic centre for medical science, education and health care with over 8,500 staff and 3,000 students.
The focus of the Diagnostic Image Analysis group is the development and validation of novel methods in a broad range of medical imaging applications. Research topics include image analysis, image segmentation, machine learning, and the design of decision support systems. Application areas include breast imaging, prostate imaging, and lung imaging. Key to the success of the group is close cooperation with clinical partners and a disease oriented approach. Currently the group consists of around 35 researchers, including 21 PhD students.
Chest CT is the best way to image the lungs in vivo. This project focuses on vascular analysis in the lungs, mainly the separation of arteries and veins. Several methods for pulmonary vessel segmentation are available (as illustrated here) but the separation into arteries and veins remains challenging due to numerous touchings and crossings of the trees that have similar appearance as bifurcations or trifurcations of the same tree. Artery-vein separation in CT scans of the lungs is important for computer-aided diagnosis and treatment planning and increases understanding of the vascular function and structure. In this project the main focus for application will be on pulmonary emboli (PE). A PE is a sudden blockage in a pulmonary artery, it is a prevalent condition (in the United States at least 650,000 cases of PE occur annually) and is a common cause of death in all age groups. In our hospital, 50 CT scans a month are taken for suspected PE. Detecting small PE is a difficult task for radiologists, therefore computer-aided detection of PE is being developed, for which a prerequisite is the separation of arteries and veins.
The main focus of the project will be:
- Development of artery-vein separation in chest CT scans
- Evaluation of the developed methodology
- Embedding in computer aided detection of PE
- Evaluation of performance of PE detection before and after artery-vein separation
This project is part of the chest CT research within DIAG. This position is funded by NWO. The project will be performed in close collaboration with Fraunhofer Mevis, Bremen, Germany. A large database of scans from both clinical practice and screening trials is available for the research. You will be part of the DIAG team that works on chest CT analysis. The team consists of technical researchers, radiologists and pulmonologists and also works on nodule detection, nodule segmentation, COPD quantification, airway analysis, and segmentation of normal anatomy.
You should be a creative and enthusiastic researcher with an MSc degree in Computer Science, Physics, Engineering or Biomedical Sciences or similar, with a clear interest to develop image analysis algorithms and an affinity with medical topics. Good communication skills and expertise in software development, preferably in C++, are essential.
Terms of employment
You will be appointed as a PhD student with the standard salary and secondary conditions for PhD students in the Netherlands. Your performance will be evaluated after 1 year. If the evaluation is positive, the contract will be extended by 3 years. The research should result in a PhD thesis.
For more information feel free to contact Dr. E. van Rikxoort by e-mail.
Send applications as a single pdf file to firstname.lastname@example.org. In this pdf file the following should be included: CV, list of followed courses and grades, letter of motivation, and preferably a reprint of your Master thesis or any publications in English you have written.