Computational Imaging is an interdisciplinary working group between TUM Neuroradiology and Image-Based Biomedical Modeling, jointly headed by Dr. Benedikt Wiestler and Prof. Bjoern Menze.

Medical Imaging generates a plethora of data, of which today only a fraction is used for clinical decision making. Within our Working Group, we aim to develop algorithms and strategies to make the wealth of information accessible to clinicians. To this end, we are developing tools for (un)supervised lesion detection / segmentation, classification and data integration. Together with our clinical partners @ TUM, our current focus is on two neurological model diseases: Multiple Sclerosis and Gliomas. To support dissemination and use of our results, we aim to make all tools developed by us available here. We are also actively contributing to important challenges (BRATS) and workshops (BrainLes) in the field of medical image computing.



Group Leaders


Collaboration Partners

Selected Projects

alt text alt text
alt text alt text
alt text alt text

Selected Publications

Li H, Paetzold J, Sekuboyina A, Kofler F, Zhang J, Kirschke JS, Wiestler B, Menze BH. DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis. MICCAI, 2019

Baur C, Wiestler B, Albarqouni S, Navab N. Fusing Unsupervised and Supervised Deep Learning for White Matter Lesion Segmentation. MIDL, 2019

Eichinger P, Schön S, Pongratz V, Wiestler H, Zhang H, Bussas M, Hoshi MM, Kirschke JS, Berthele A, Zimmer C, Hemmer B, Mühlau M, Wiestler B. Accuracy of Unenhanced MRI in the Detection of New Brain Lesions in Multiple Sclerosis. Radiology, 2019

Lipkova J, Angelikopoulos P, Wu S, Alberts E, Wiestler B, Diehl C, Preibisch C, Pyka T, Combs S, Hadjidoukas P, Van Leemput K, Koumoutsakos P, Lowengrub JS, Menze BH. Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans and Bayesian Inference. IEEE TMI, 2019

Molina-Romero M, Wiestler B, Gómez PA, Menzel MI, Menze BH. Deep Learning with Synthetic Diffusion MRI Data for Free-Water Elimination in Glioblastoma Cases. MICCAI, 2018

Zhang H, Alberts E, Pongratz V, Mühlau M, Zimmer C, Wiestler B, Eichinger P. Predicting conversion from clinically isolated syndrome to multiple sclerosis–An imaging-based machine learning approach. NeuroImage: Clinical, 2018

Baur C, Wiestler B, Albarqouni S, Navab N. Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images. arXiv:1804.04488, 2018

Eichinger P, Wiestler H, Zhang H, Biberacher V, Kirschke JS, Zimmer C, Mühlau M, Wiestler B. A novel imaging technique for better detecting new lesions in multiple sclerosis. J Neurol, 2017

Eichinger P, Alberts E, Delbridge C, Trebeschi S, Valentinitsch A, Bette S, Huber T, Gempt J, Meyer B, Schlegel J, Zimmer C, Kirschke JS, Menze BH, Wiestler B. Diffusion tensor image features predict IDH genotype in newly diagnosed WHO grade II/III gliomas. Scientific Reports, 2017

Alberts E, Tetteh G, Trebeschi S, Bieth M, Valentinitsch A, Wiestler B, Zimmer C, Menze BH. Multi-modal Image Classification Using Low-Dimensional Texture Features for Genomic Brain Tumor Recognition. MICGen @ MICCAI 2017

Menze BH, Van Leemput K, Lashkari D, Riklin-Raviv T, Geremia E, Alberts E, Gruber P, Wegener S, Weber MA, Székely G, Ayache N, Golland P. A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation - With Application to Tumor and Stroke. IEEE Trans. Med. Imaging, 2016

Osswald M, Jung E, Sahm F, Solecki G, Venkataramani V, Blaes J, Weil S, Horstmann H, Wiestler B, Syed M, Huang L, Ratliff M, Karimian Jazi K, Kurz FT, Schmenger T, Lemke D, Gömmel M, Pauli M, Liao Y, Häring P, Pusch S, Herl V, Steinhäuser C, Krunic D, Jarahian M, Miletic H, Berghoff AS, Griesbeck O, Kalamakis G, Garaschuk O, Preusser M, Weiss S, Liu H, Heiland S, Platten M, Huber PE, Kuner T, von Deimling A, Wick W, Winkler F. Brain tumour cells interconnect to a functional and resistant network. Nature, 2015

Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R, Lanczi L, Gerstner E, Weber MA, Arbel T, Avants BB, Ayache N, Buendia P, Collins DL, Cordier N, Corso JJ, Criminisi A, Das T, Delingette H, Demiralp Ç, Durst CR, Dojat M, Doyle S, Festa J, Forbes F, Geremia E, Glocker B, Golland P, Guo X, Hamamci A, Iftekharuddin KM, Jena R, John NM, Konukoglu E, Lashkari D, Mariz JA, Meier R, Pereira S, Precup D, Price SJ, Raviv TR, Reza SM, Ryan M, Sarikaya D, Schwartz L, Shin HC, Shotton J, Silva CA, Sousa N, Subbanna NK, Szekely G, Taylor TJ, Thomas OM, Tustison NJ, Unal G, Vasseur F, Wintermark M, Ye DH, Zhao L, Zhao B, Zikic D, Prastawa M, Reyes M, Van Leemput K. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans. Med. Imaging, 2015


We are supported by the SFB-824, Deutsche Krebshilfe, TUM-KKF, ZD.B and DFG.