Benedikt Wiestler is a resident in the Department of Neuroradiology at Klinikum rechts der Isar, TUM. He was a postdoc researcher at DKFZ Heidelberg, working on bioinformatics in glioma (epi)genomics. Currently, he is researching (deep) learning strategies for image analysis.
Jan Kirschke is currently attending (neuro)radiologist and member of the faculty at the department of neuroradiology at the “Technische Universität München” in Munich, Germany. He started working on high resolution imaging in 2001 and is currently focusing on quantitative imaging, image based biomechanical modelling and imaging in neurooncology.
Within our Group, we establish quantitative imaging to visualize, describe and model pathophysiology in neurooncology. 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. We evaluate the utility of these tools for better disease characterization and outcome prediction, in particular in gliomas.
- 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
- 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
- Hedderich D, Kluge A, Pyka T, Zimmer C, Kirschke JS, Wiestler B, Preibisch C. Consistency of normalized cerebral blood volume values in glioblastoma using different leakage correction algorithms on dynamic susceptibility contrast magnetic resonance imaging data without and with preload. J Neuroradiol. 2019
- Huber T, Rotkopf L, Wiestler B, Kunz WG, Bette S, Gempt J, Preibisch C, Ricke J, Zimmer C, Kirschke JS, Sommer WH, Thierfelder KM. Wavelet-based reconstruction of dynamic susceptibility MR-perfusion: a new method to visualize hypervascular brain tumors. Eur Radiol. 2019 May;29(5):2669-2676.
- 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
- 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, 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
- Bette S, Huber T, Gempt J, Boeckh-Behrens T, Wiestler B, Kehl V, Ringel F, Meyer B, Zimmer C, Kirschke JS. Local Fractional Anisotropy Is Reduced in Areas with Tumor Recurrence in Glioblastoma. Radiology. 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