| Alessandro Sbrizzi is an assistant professor at the Imaging division of the UMC Utrecht. He graduated in Scientific Computing and obtained his PhD in 2013 with a thesis focused on modelling and numerical optimization of MRI acquisition & reconstruction processes. He is a recipient of an NWO-VENI grant 2016 and an NWO Demonstrator grant 2018. |
 | Hugo Kuijf is an assistant professor at the Image Sciences Institute, UMC Utrecht and university lecturer at Eindhoven University of Technology. He graduated in Computer Science at Utrecht University in 2009 and received his PhD in Medical Imaging after defending his thesis entitled "Image processing techniques for quantification and assessment of brain MRI" in 2013. His research focuses on innovative image processing and (deep) machine learning techniques for the quantification and assessment of brain MR images. |
 | Niek Huttinga is a PhD student at the UMC Utrecht, under supervision of Alessandro Sbrizzi and Nico van den Berg. He obtained a Bachelor's and Master's degree in Applied Mathematics and his thesis focused on theory and applications of deep learning. His PhD focuses on model-based iterative motion estimation for the purpose of MR-guided radiotherapy, and he is exploring the use of deep learning in this context. |
 | Louis van Harten is a PhD candidate at the University of Amsterdam. He has worked on different applications of deep learning in the radiotherapy workflow, including automatic OAR segmentation in head MR and thoracic CT images, as well as synthetic CT generation and automatic quality control for an MR-only radiotherapy workflow. His current research focuses on using deep learning to automatically characterize gastrointestinal motility in dynamic MR images of the abdomen. |
 | Mark Savenije is a scientific programmer having a somewhat nomadic career moving accross several dutch institutes and universities. Funny enough, for the first time he works on topics he should be somewhat knowledgeable about as he graduated in the early 90's on the development of a self-adaptive growing neural network (called the 'Muppet algorithm') which unfortunately did not survive a computer crash and is waiting to be rediscovered. In the mean time he investigates where deep learning techniques can be employed in the RT workflow (organ/CTV segmentation, synthetic-CT's, CBCT correction). |
 | Nico van den Berg is an MRI-physicist working as an Associate Professor at the Imaging Division in the Department of radiotherapy. He heads the newly formed group "Computational Imaging" that focuses on the development and application of new MR image acquisition, reconstruction and processing techniques for radiotherapy and diagnostic applications. In this context, deep learning applications for image manipulations and information extraction became in the last years a key technology in the group's research. |
 | Matteo Maspero is a physicist enrolled as a postdoctoral researcher and clinical scientist at the radiotherapy Department of UMC Utrecht. His primary focus is deep learning for adaptive radiotherapy and image processing. Specifically, he uses deep learning trying to address image synthesis problem and dreaming (or having nightmares) about how to automatically QA the results within a clinical framework. Matteo is known for hyper-coloured presentations, let's see if he will keep up with the expectations... ORCID: https://orcid.org/0000-0003-0347-3375 |
 | Koen Eppenhof is a PhD student in the Medical Image Analysis Group at Eindhoven University of Technology. In his PhD, he focuses on supervised image registration techniques based on deep learning, with applications to MR-guided prostate radiotherapy. |
 | Charis Kontaxis is a clinical computer scientist at the radiotherapy Department of University Medical Center Utrecht. His PhD was focused on online treatment plan adaptation for MRI-guided radiotherapy. He is now exploring deep learning applications in the treatment planning process and working towards clinical implementation of adaptive radiotherapy workflows. |
 | Bjorn Stemkens is a postdoctoral researcher at the Department of radiotherapy of the University Medical Center Utrecht. He obtained his PhD in 2017 where he focussed on the development of MRI methods for MRI-guided radiotherapy. He is exploring deep learning applications for MRI-guided radiotherapy with a specific focus on accelerated MR image reconstruction, automation, and integration into the radiotherapy workflow. |
 | Gijsbert Bol is a clinical computer scientist at the radiotherapy Department of the University Medical Center Utrecht. His PhD was focused on developing online treatment planning strategies for MRI-guided radiotherapy. He has extensive experience with translating research software into the clinic, from delineation and treatment planning packages to deeplearning based autosegmentation software. He also has knowledge about the regulatory implications involved with using homebrew software in a clinical environment. |
 | After obtaining her PhD on prostate brachytherapy dose optimization (at the AMC), Anna Dinkla worked as a postdoctoral researcher at the Department of radiotherapy of the UMC in Utrecht on MR-only treatment planning for brain and head-and-neck radiotherapy. In collaboration with the Imaging Sciences Institute (ISI) she studied the use of synthetic-CT for radiotherapy, generated with deep (convolutional) neural networks. She recently started as a medical physicist in training at the radiotherapy department in Amsterdam (VUMC). |
 | Joost Verhoeff MD PhD is a radiation oncologist treating brain tumors and lung cancer at the Radiotherapy Department of University Medical Center Utrecht. He is member of the UMCU Electronic Medical Record control group and leader of the research groups for brain tumors and lung cancer. He wants the best treatment for every patient, and hates doing repetitive work. Therefore he’s very interested in deep learning. |