Software:

  • S3: Skull stripping and brain tissue segmentation tool
  • Segmentation Pipeline: Software for automated segmentation of brain lesions from multimodal scans with an interactive tool for easy segmentation corrections (Available upon request, official release soon)
  • RT2nii: Software for converting radiotherapy plans from dicom to nifty
  • Pi4u: high performance computing framework for Bayesian uncertainty quantification
  • TORC task-parallel library for clusters
  • Registration: Tool for easy image registration using ANTs.

Visualisations Tools

  • ITKSnap Software for visualisation and semi-automated segmentation of medical scans.
  • Paraview Software for visualising output from the GliomaSolver simulation (*.vtu, nifty) and others. Allows also 3D rendering.
  • Vis: Matlab tool for automated visualising multiple medical data.

Data

  • Sample data for the GliomaSolver
  • Multimodal scans of brain lesions provided by BRATS
  • Brain Atlas: Normal adult brain anatomy atlas
  • Data of glioblastoma patients released in conjuction with Lipkova et al. paper are available here

Publications

2018

  1. Lipkova, J., Angelikopoulos, P., Wu, S., Alberts, E., Wiestler, B., Diehl, C., … others. (2018). Personalized Radiotherapy Planning for Glioma Using Multimodal Bayesian Model Calibration. ArXiv Preprint ArXiv:1807.00499.

2015

  1. Rossinelli, D., Hejazialhosseini, B., van Rees, W., Gazzola, M., & others. (2015). MRAG-I2D: Multi-resolution adapted grids for remeshed vortex methods on multicore architectures. Journal of Computational Physics.
  2. Hadjidoukas, P. E., Angelikopoulos, P., Papadimitriou, C., & Koumoutsakos, P. (2015). \Pi4U: A high performance computing framework for Bayesian uncertainty quantification of complex models. Journal of Computational Physics.

2012

  1. Hadjidoukas, P. E., Lappas, E., & Dimakopoulos, V. V. (2012). A runtime library for platform-independent task parallelism. In Parallel, Distributed and Network-Based Processing (PDP), 2012 20th Euromicro International Conference on (pp. 229–236). IEEE.

2007

  1. Ching, J., & Chen, Y.-C. (2007). Transitional Markov chain Monte Carlo method for Bayesian model updating, model class selection, and model averaging. Journal of Engineering Mechanics.

Acknowledgment

  • Benedikt Wiestler (Klinkum Rechts der Isar TUM) - for the clinical inputs
  • Panagiotis Hadjidoukas (IBM) - for the help with TORC
  • Luca Baldesi (University of Trento) - for the help with this website

Contact

If you have any questions or need help, just let us know