In 2012 Bob finished his Master Biomedical Engineering at the University of Groningen (RUG). During his study Bob focused on clinical physics and became interested in medical image processing. After a couple of temporary assignments in the industry. Bob got the opportunity to be a PhD-candidate at the ISI. His main focus is on finding biomarkers in CT lung cancer screening scans to predict cardiovascular risk.
Bob is co-organizer of the MICCAI Challenge on Automatic Coronary Calcium Scoring.
Papers in conference proceedings
|1.||Automatic segmentation of thoracic aorta segments in low-dose chest CT. In: SPIE Medical Imaging, in press, 2018.|
|2.||End-to-end unsupervised deformable image registration with a convolutional neural network. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third International Workshop, DLMIA 2017ML-CDS 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, Proceedings , 2017, pp. 204–212.|
|3.||Nonrigid image registration using multi-scale 3D convolutional neural networks. In: Medical Image Computing and Computer Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part 1, 2017, vol. 10433, pp. 232–239.|
|4.||Classification of coronary artery calcifications according to motion artifacts in chest CT using a convolutional neural network. In: SPIE Medical Imaging, 2017.|
|5.||Automatic Slice Identification in 3D Medical Images with a ConvNet Regressor. In: Deep Learning and Data Labeling for Medical Applications: First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, MICCAI 2016, Athens, Greece, 2016, pp. 161–169.|
|6.||2D image classification for 3D anatomy localization; employing deep convolutional neural networks. In: SPIE Medical Imaging, 2016, vol. 9784, pp. 97841Y-1-97841Y-7.|
|7.||Genome-Wide Association Study of Coronary and Aortic Calcification in Lung Cancer Screening CT. In: SPIE Medical Imaging, 2016, vol. 9784, pp. 97841L-1-97841L-6.|
|8.||Automatic detection of cardiovascular risk in CT attenuation correction maps in Rb-82 PET/CTs. In: SPIE Medical Imaging, 2016, vol. 9784, pp. 978405-1-978405-6.|
|9.||Deep convolutional neural networks for automatic coronary calcium scoring in a screening study with low-dose chest CT. In: SPIE Medical Imaging, 2016, vol. 9785, pp. 978511-1-978511-6.|
|10.||Automatic segmentation of the left ventricle in cardiac CT angiography using convolutional neural networks. In: IEEE International Symposium on Biomedical Imaging, 2016, pp. pp. 40-43.|
|11.||Automatic machine learning based prediction of cardiovascular events in lung cancer screening data. In: SPIE Medical Imaging, 2015, vol. 9414, pp. 94140D.|
|1.||Direct coronary artery calcium scoring in low-dose chest CT using deep learning analysis. Radiological Society of North America, 103rd Annual Meeting, 2017.|
|2.||Increasing the Interscan Reproducibility of Coronary Calcium Scoring by Partial Volume Correction in Low-Dose non-ECG Synchronized CT: Phantom Study. Radiological Society of North America, 2015.|