Spine Imaging
Disease Prediction

We study new imaging techniques in CT and MRI for quantitative imaging of the spine. We develop automated evaluation algorithms including artificial intelligence, deep learning and biomechanic modeling to predict disease progression and get new pathophysiologic insights. Currently we focus on osteoporosis, back pain and degenerative spine diseases.


Quantitative imaging

We optimize both CT and MRI protocols to derive quantitative tissue parameters of the spine. Recently we investigated novel iterative CT reconstruction techniques and sparse sampling acquisition to lower radiation dose in bone mineral density measurements. In MRI we demonstrated that diffusion tensor imaging (DTI) correlates well with muscle strength.

Automatic segmentation

We develop vision- and learning-based algorithms capable of locating, identifying, and segmenting vertebrae in CT and MR scans as well as in radiographs. We intend to open-source our processing algorithms to the computer science and medical communities.

Opportunistic Screening in Osteoporosis

We develop automated techniques for opportunistic BMD measurements in any CT dataset and patient specific evaluation of local bone loss using voxel based morphometry (VBM). We also implemented automatic fracture detection algorithms using artificial intelligence.

Biomechanic modeling

To better understand back pain and surgical outcome, we combine quantitative parameters from different imaging modalities (X-rays, MRI, CT) in one biomechanical model, based on multibody simulations (MBS) on a large scale and finite element models (FEM/FCM) on a small scale.

Selected Publications

  • Löffler MT, Jacob A, Valentinitsch A, Rienmüller A, Zimmer C, Ryang YM, Baum T, Kirschke JS. Improved prediction of incident vertebral fractures using opportunistic QCT compared to DXA. Eur Radiol. 2019 Feb 21. doi: 10.1007/s00330-019-06018-w. [Epub ahead of print] PubMed PMID: 30790025.
  • Valentinitsch A, Trebeschi S, Kaesmacher J, Lorenz C, Löffler MT, Zimmer C, Baum T, Kirschke JS. Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures. Osteoporos Int. 2019 Mar 4. doi: 10.1007/s00198-019-04910-1. [Epub ahead of print] PubMed PMID: 30830261.
  • Sollmann N, Mei K, Hedderich DM, Maegerlein C, Kopp FK, Löffler MT, Zimmer C, Rummeny EJ, Kirschke JS, Baum T, Noël PB. Multi-detector CT imaging: impact of virtual tube current reduction and sparse sampling on detection of vertebral fractures. Eur Radiol. 2019 Mar 22. doi: 10.1007/s00330-019-06090-2. [Epub ahead of print] PubMed PMID: 30903337.
  • Burian E, Rohrmeier A, Schlaeger S, Dieckmeyer M, Diefenbach MN, Syväri J, Klupp E, Weidlich D, Zimmer C, Rummeny EJ, Karampinos DC, Kirschke JS, Baum T. Lumbar muscle and vertebral bodies segmentation of chemical shift encoding-based water-fat MRI: the reference database MyoSegmenTUM spine. BMC Musculoskelet Disord. 2019 Apr 9;20(1):152. doi: 10.1186/s12891-019-2528-x. PubMed PMID: 30961552.
  • Klupp E, Cervantes B, Schlaeger S, Inhuber S, Kreuzpointer F, Schwirtz A, Rohrmeier A, Dieckmeyer M, Hedderich DM, Diefenbach MN, Freitag F, Rummeny EJ, Zimmer C, Kirschke JS, Karampinos DC, Baum T. Paraspinal Muscle DTI Metrics Predict Muscle Strength. J Magn Reson Imaging. 2019 Feb 5. doi: 10.1002/jmri.26679. [Epub ahead of print] PubMed PMID: 30723976.
  • Baum T, Lorenz C, Buerger C, Freitag F, Dieckmeyer M, Eggers H, Zimmer C, Karampinos DC, Kirschke JS. Automated assessment of paraspinal muscle fat composition based on the segmentation of chemical shift encoding-based water/fat-separated images. Eur Radiol Exp. 2018 Nov 7;2(1):32. doi: 10.1186/s41747-018-0065-2. PubMed PMID: 30402701; PubMed Central PMCID: PMC6219990.
  • Hedderich DM, Maegerlein C, Baum T, Hapfelmeier A, Ryang YM, Zimmer C, Kirschke JS. Differentiation of Acute/Subacute versus Old Vertebral Fractures in Multislice Detector Computed Tomography: Is Magnetic Resonance Imaging Always Needed? World Neurosurg. 2019 Feb;122:e676-e683. doi: 10.1016/j.wneu.2018.10.121. Epub 2018 Oct 29. PubMed PMID: 30385360.
  • Anitha D, Subburaj K, Kopp FK, Mei K, Foehr P, Burgkart R, Sollmann N, Maegerlein C, Kirschke JS, Noel PB, Baum T. Effect of Statistically Iterative Image Reconstruction on Vertebral Bone Strength Prediction Using Bone Mineral Density and Finite Element Modeling: A Preliminary Study. J Comput Assist Tomogr. 2019 Jan/Feb;43(1):61-65. doi: 10.1097/RCT.0000000000000788. PubMed PMID: 30211797.


Jan S. Kirschke
Principal Invesitigator
Thomas Baum
Co-Principal Investigator
Maximilian T. Löffler
Anjany Sekuboyina
PhD Student
Amir Bayat
PhD Student
Tanja Lerchl
PhD Student
Mohamed Elhaddad
PhD Student
Malek Husseini
PhD Student
Alina Jakob
Anna-Lena Grau


The European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (637164 — iBack — ERC-2014-STG) 2015-2020
The German Research Foundation 2010-2018
The German Federal Ministry for Economic Affairs and Energy 2012-2015

Abteilung für Neuroradiologie Klinikum rechts der Isar

Technische Universität München, Ismaninger Str. 22, 81675 München

Center for Translational Cancer Research (TranslaTUM)

Technische Universität München, Einsteinstraße 25, 81675 München


The Large Scale Vertebrae Segmentation Challenge Series


Key highlights

  • Held at MICCAI 2019, Shenzhen, China.
  • Public release of an unprecedented 160 spine CT scans with voxel-level vertebral annotations.
  • 500+ data download requests!
  • Benchmarked 20 labelling and segmentation algorithms.
  • Dataset released under CC BY-SA 2.0 license.


Key highlights

  • To be held at MICCAI 2020, Lima, Peru.
  • Public release of an unprecedented 300 spine CT scans with voxel-level vertebral annotations.
  • Multi-center, multi-scanner dataset.
  • Focus on peculiar spinal anatomies incl. transitional vertebrae.
  • Dataset released under CC BY-SA 4.0 license.