DACHMM

Whole body image analysis for diagnosing patients with monoclonal plasma cell disorders, funded by Deutsche ForschungsGesellschaft (DFG) and Austrian Science Fund (FWF)

Team: Georg Langs, Johannes Hofmanninger, Roxane Licandro, Matthias Perkonigg

Partner: Bjoern Menze, Marc-André Weber

Multiple myeloma (MM) is a blood cancer, which affects the generation pathway of plasma cells, resulting in uncontrolled proliferation and malignant transformation of these. This project aims at modelling infiltration patterns of MM longitudinally over progression time, and at automatically measuring disease progression and treatment response based on multi-modal imaging data. The main components of this project are lesion detection and segmentation, growth modelling and measurement of disease progression.

Recent publications:

  • Licandro R., Hofmanninger J., Perkonigg M., Roehrich S., Weber M.-A., Wennmann M., Kintzele L., Bjoern M., Langs G., “Evolution Risk Prediction of Bone Lesions in Multiple Myeloma”, European Congress of Radiology (ECR), March 2020.
  • Licandro R., Hofmanninger J., Perkonigg M., Röhrich S., Weber M.-A., Wennmann M., Kintzele L., Piraud M., Menze B., Langs G., Asymmetric Cascade Networks for Focal Bone Lesion Prediction in Multiple Myeloma, International Conference on Medical Imaging with Deep Learning (MIDL), London, July 2019. (Report number: MIDL/2019/Extended Abstract/H1xLm6fQ5E)
  • Licandro R., Hofmanninger J., M.-A. Weber, B. Menze, G. Langs, Whole-body MRI based lesion prediction in multiple myeloma, European Congress of Radiology (ECR) 2019, Vienna, February 2019. Link
  • (Eds.) A. Melbourne and R. Licandro et al., Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis, LNCS volume 11076, Springer, Cham, ISBN: Print ISBN 978-3-030-00806-2, DOI: https://doi.org/10.1007/978-3-030-00807-9, September 2018.
  • Perkonigg M., Hofmanninger J., Menze B., Weber MA., Langs G. (2018) Detecting Bone Lesions in Multiple Myeloma Patients Using Transfer Learning. In: Melbourne A. et al. (eds) Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis. PIPPI 2018, DATRA 2018. Lecture Notes in Computer Science, vol 11076. Springer, Cham
  • Licandro R., Hofmanninger J., M.-A. Weber, B. Menze, G. Langs, Predicting Future Bone Infiltration Patterns in Multiple Myeloma, Proceedings 4th International Workshop on Patch-based Techniques in Medical Imaging (MICCAI Patch-MI 2018), Granada (Spain), September 2018.PDF
  • Licandro R., J. Hofmanninger, M.-A. Weber, B. Menze, G. Langs, Early Predictors of Bone Infiltration in Multiple Myeloma Patients from T2 weighted MRI images, Proceedings of the 42nd Austrian Association for Pattern Recognition (OAGM/AAPR) workshop, pages 9-12, Hall in Tyrol (Austria), May 2018, DOI: 10.3217/978-3-85125-603-1 PDF
  • Licandro R., J. Hofmanninger, B. Menze, MA. Weber, G. Langs, ​Whole​ ​body​ ​image​ ​analysis​ ​for​ ​diagnosing​ ​patients​ ​with monoclonal​ ​plasma​ ​cell​ ​disorders, European Project Space on Intelligent Systems and Machine Learning (EPS-IST), 7th International Conference on Pattern Recognition Applications and Methods, Funchal - Madeira (Portugal), January 2018.
  • Wachinger, C., Toews, M., Langs, G., Wells, W. and Golland, P., 2018. Keypoint transfer for fast whole-body segmentation. IEEE Transactions on Medical Imaging.
  • Langs, G., Röhrich, S., Hofmanninger, J., Prayer, F., Pan, J., Herold, C. and Prosch, H., 2018. Machine learning: from radiomics to discovery and routine. Der Radiologe, pp.1-6.
  • Hofmanninger J., Menze B., Weber MA., Langs G. Mapping Multi-Modal Routine Imaging Data to a Single Reference via Multiple Templates. In: Cardoso M. et al. (eds) Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. DLMIA 2017, ML-CDS 2017. Lecture Notes in Computer Science, vol 10553. Springer, Cham. 2017.