WBIR 2022 Awards

A committee of three experts: Prof. Nassir Navab (chair), Dr. Wolfgang Wein and Prof. Maria Vakalopoulou selected the best scientific paper under all accepted works WBIR. They took into consideration both the quality of the written manuscript as well as the presentation. The winner was

Anton François et al (University Paris Decartes, France)
-   Weighted Metamorphosis for registration of images with different topology

The two runner-ups where

Samuel Joutard et al (King’s College London, London) A multi-organ point cloud registration algorithm for abdominal CT registration
Ubaldo Ramon Julvez et al (University of Zaragoza) LDDMM meets GANs: Generative Adversarial Networks for diffeomorphic registration 

We congratulate all winners for their achievements.

In addition, we awarded an audience prize for which all participants could cast one vote each: The winner was

Andreas Smolders: et al.: (Paul Scherrer Institute, Villingen Switzerland)

- Deformable Image Registration uncertainty quantification using deep learning for dose accumulation in adaptive proton therapy

The runner up was: Julia Andresen et al (University of Lübeck): Unsupervised Non-Correspondence Detection in Medical Images Using an Image Registration Convolutional Neural Network


Wolfgang Wein


Wolfgang Wein has worked in medical image computing research for the last 19 years, conducting numerous projects from early feasibility to product implementation. He received his doctoral degree in 2007 from Prof. Navab at the group for Computer Aided Medical Procedures (CAMP) at TU Munich, Germany. From 2006-2010 he worked as a research scientist at Siemens Corporate Research in Princeton, NJ USA, on various interventional navigation and medical imaging projects. In 2012, he founded the R&D lab ImFusion in Munich, which aids medical device companies around the world to create innovation. He also has a teaching assignment at TU Munich.


"Combining visual computing with machine learning for improved registration in image-guided interventions"

Josien Pluim


Josien Pluim is professor of Medical Image Analysis at Eindhoven University of Technology, with a joint appointment at the UMC Utrecht, for one day a week. She is head of the Medical Image Analysis group (IMAG/e, www.tue-image.nl). Her research focus is on image analysis (e.g. registration, segmentation, detection, machine/deep learning), both methodology development and clinical applications. The latter in particular targeted at neurology and oncology. Most of the research is performed in cooperation with clinical partners and/or industry.
Josien Pluim is or was associate editor of five journals (IEEE TMI, IEEE TBME, Medical Physics, Journal of Medical Imaging and Medical Image Analysis). She served as a member of the Executive Board of the MICCAI Society, as conference chair of SPIE Medical Imaging Image Processing 2006-2009, chair of WBIR 2006 and programme co-chair of MICCAI 2010.  She is a MICCAI Society and IEEE Fellow.



Maria Vakalopoulou


Maria Vakalopoulou is an Assistant Professor at MICS laboratory of CentraleSupelec, University Paris-Saclay, Paris, France. She is also the team lead of the βiomathematics group of MICS laboratory.  Her research is focusing on machine and deep learning with applications on health care and in particular medical image analysis. She is currently working on the topics of medical image registration and classification for medical images of different scales (histopathology, CT, MRI) in close collaboration with clinicians towards development of robust algorithms for prognosis, diagnosis and disease modeling focusing on breast and lung cancer as well as different interstitial lung diseases. Her research has been published in international journals and conferences (Lancet Oncology, Radiology, European Radiology, Medical Image Analysis, MICCAI). Moreover, she has served as an area chair for CVPR 2021, 2022, MICCAI 2022 and MIDL 2020 and 2021 conferences and as a guest editor in the CVIU journal of Elsevier.


"Classical and deep learning based registration and its impact to clinical diagnosis"