HORIZON-MSCA-2021-DN-01-01 (DC3) - Evolutionary quantification of breast density after surgery or...

Updated: 3 months ago
Job Type: FullTime
Deadline: 11 Sep 2022

Prof. Ioannis Sechopoulos and Dr. Marco Caballo, part of the Advanced X-ray Tomographic Imaging (AXTI) laboratory, established at the Department of Medical Imaging at Radboud University Medical Center (Nijmegen, the Netherlands), are interested in receiving Expressions of Interest of potential candidates for the Marie Sklodowska – Curie Doctorate Network (MSCA-DN-2021) call.

The title of the proposed project: Evolutionary quantification of breast density after surgery or radiotherapy for local recurrence detection

The overarching goal of this project is directed to the detection of local recurrence (probability of relapse) through the quantitative changes perceived in breast density (fibrosis) after applying different radiotherapy techniques (hypofractionation or control mammography). Besides, we will investigate the correlation of such quantification as a potential measure to predict genetic susceptibility to adverse effects of radiotherapy using DNA samples from selected cases. This will be performed using the following steps: (1) Developing a DL model based on a conditional GAN to implement dense tissues segmentation in mammographic images (i.e., 2D mammography or pseudo-3D tomosynthesis). (2) Developing a multi-class CNN architecture for breast density classification using the resulted segmented masks based on BI-RADS standard (i.e., fatty, scattered fibroglandular, heterogeneously, and extremely). (3) Constructing a fully automatic method for breast density estimation. (4) Improving breast density estimation that might be achieved by using alternative breast imaging, such as MRI. (5) Studying the relationship between breast density, the genetic susceptibility to BC relapse and the effects of radiotherapy treatment. (6) a fully automatic follow-up of the quantification of breast density of multiple temporal mammograms for estimating the risk of the relapse.

For BosomShield, we are looking for a doctorate candidate who will:

  • collaborate with diverse teams of scientists and engineers to build an advanced system for BosomShield.
  • design and develop cutting edge methods based on brittle computer vision and machine learning algorithms.
  • help advance R&D by finding problems, implementing elegant solutions, and building tools that enable the team to move forward and to measure progress.
  • present at international scientific conferences and write papers on newly developed methods and the study results.

We are looking for talented and innovative self-motivated young scientists, strongly committed to high quality frontier and multi-disciplinary research and able to add new insights to the existing Radboud University Medical Center core expertise.

Radboud University Medical Center as Hosting Institution located Nijmegen, the Netherlands, has all the technical and scientific facilities to carry out this project.

Nijmegen and the Radboudumc

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 Medical Center is a leading academic center for medical science, education and health care with over 10,000 staff and 3,000 students.

Department of Medical Imaging

The Department of Medical Imaging is one of the most active research departments of the Radboudumc. More than 100 researchers are continuously striving to optimize healthcare. Close cooperation between preclinical researchers, radiologists and cardiologists gives a fantastic opportunity to implement new imaging techniques in clinical practice, and you will be working directly in this field.

Advanced X-ray Tomographic Imaging laboratory

The AXTI laboratory is focused on the development, optimization, and clinical evaluation of new x-ray-based imaging methods. The lab focuses on the use of medical physics approaches to improve image acquisition methods and reconstruction and processing, including artificial intelligence algorithms, and to evaluate the clinical performance of new technology in new clinical applications. The group counts with wide-ranging expertise in image processing, reconstruction, deep learning and other AI methods, and analysis, as well as radiation dosimetry, along with the design and performance of patient trials.

The laboratory works closely with radiologists and industry to develop new, clinically relevant, imaging methods and processes in x-ray-based imaging, especially in breast imaging and body CT, to have an impact in detection, diagnosis, and treatment of pathologies.

More information can be found on our website: http://axti.radboudimaging.nl/index.php/Home .


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