Research line on artificial intelligence in imaging
Research line
Research is focused on development of artificial intelligence methods for analysis of medical images to enable automatic patient risk profiling, diagnosis and prognosis, mainly applied to cardiovascular disease.
Research team
Faculty
Post-docs
PhD students
Louis van Harten
Marinka Oudkerk Pool
Riaan Zoetmulder
Dimitrios Karkalousos
Sanne van Velzen
Nadieh Khalili
Key publications
- Deep Learning for Automatic Calcium Scoring in CT: Validation Using Multiple Cardiac CT and Chest CT Protocols. van Velzen SGM, Lessmann N, Velthuis BK, Bank IEM, van den Bongard DHJG, Leiner T, de Jong PA, Veldhuis WB, Correa A, Terry JG, Carr JJ, Viergever MA, Verkooijen HM, Išgum I. Radiology. 2020 Apr;295(1):66-79.
- Generative adversarial networks for noise reduction in low-dose CT. JM Wolterink, T Leiner, MA Viergever, I Išgum IEEE transactions on medical imaging 36 (12), 2536-2545
- A deep learning framework for unsupervised affine and deformable image registration. de Vos BD, Berendsen FF, Viergever MA, Sokooti H, Staring M, Išgum I. Med Image Anal. 2019 Feb;52:128-143.
- Sex Differences in Coronary Artery and Thoracic Aorta Calcification and Their Association With Cardiovascular Mortality in Heavy Smokers. Lessmann N, de Jong PA, Celeng C, Takx RAP, Viergever MA, van Ginneken B, Išgum I. JACC Cardiovasc Imaging. 2019 Sep;12(9):1808-1817.
- Automatic Segmentation of MR Brain Images With a Convolutional Neural Network. Moeskops P, Viergever MA, Mendrik AM, de Vries LS, Benders MJ, Išgum I. IEEE Trans Med Imaging. 2016 May;35(5):1252-126