Medical Image Registration - Linking Registration Algorithm and User

This project is funded by the open technology program of STW, grant number 13351.

September 2014 – September 2018

Principal investigator: dr. Marius Staring

 

Image registration is an important technique in the field of medical image processing, and has been used to align data from different imaging modalities, time points, and subjects. To date little of the developed technology has been adopted in clinical research and radiological workstations developed by industry. We contend that three main barriers prevent its adoption, related to the feedback of reliability and precision from the algorithm, limited user-interaction possibilities, and a lack of uniform software access to the various methodological approaches. In this project we aim to remove these barriers in a coordinated effort by two academic institutes and three companies. Novel methods are proposed to address the technical challenges by means of classification, regression and constrained optimization techniques, further strengthened by the development of continuously a benchmarked, unifying registration software. Adoption is further increased by companies integrating the results in their products.

This is a joint project between LKEB, LUMC and the Biomedical Imaging Group Rotterdam, Erasmus MC.

Associated researchers

Publications

  • D. de Vos, F. Berendsen, M.A. Viergever, M. Staring and I. Išgum, "End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network," Deep Learning in Medical Image Analysis Workshop at MICCAI, Lecture Notes in Computer Science, vol. 10553, pp. 204 - 212, September 2017. 
  • H. Sokooti, B. de Vos, F. Berendsen, B.P.F. Lelieveldt, I. Išgum and M. Staring, "Nonrigid Image Registration Using Multi-Scale 3D Convolutional Neural Networks," Medical Image Computing and Computer-Assisted Intervention, Lecture Notes in Computer Science, vol. 10433, pp. 232 - 239, September 2017.
  • H. Sokooti, G. Saygili, B. Glocker, B.P.F. Lelieveldt and M. Staring, "Accuracy Estimation for Medical Image Registration Using Regression Forests," Medical Image Computing and Computer-Assisted Intervention, Lecture Notes in Computer Science, vol. 9902, pp. 107 - 115, October 2016.
  • K. Marstal, F. Berendsen, M. Staring and S. Klein, "SimpleElastix: A user-friendly, multi-lingual library for medical image registration," International Workshop on Biomedical Image Registration (WBIR), IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 574 - 582, July 2016.
  • F. Berendsen, K. Marstal, S. Klein and M. Staring, "The design of SuperElastix - a unifying framework for a wide range of image registration methodologies," International Workshop on Biomedical Image Registration (WBIR), IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 498 - 506, July 2016.
  • M.A. Viergever, J.B.A. Maintz, S. Klein, K. Murphy, M. Staring and J.P.W. Pluim, "A survey of medical image registration - under review," Medical Image Analysis, vol. 33, pp. 140 - 144, October 2016
  • G. Saygili, M. Staring and E.A. Hendriks, "Confidence Estimation for Medical Image Registration Based On Stereo Confidences," IEEE Transactions on Medical Imaging, vol. 35, no. 2, pp. 539 - 549, February 2016.