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, degenerative spine diseases and inflammatory disorders like multiple sclerosis.



A fully automated bone screening software

Anduin is a freely available web-based application able to fully automatically segment the full spine in CT scans detect vertebral fractures and measure bone mineral density. Try it out!

The VerSe Challenges

Driving forward an interest for fully automated segmentation of the spine in the research community, we publicly released voxel-level-annotated CT data of more than 300 patients and organised the VerSe (Large Scale Vertebrae Segmentation) challenge series MICCAI 2019 and 2020.

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.

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.

Spinal Multiple Sclerosis

We investigate different techniques to automatically assess spinal lesion load and its clinical relevance for the individual patient.

Selected Publications

  • Sekuboyina A et al. VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
    ArXiv Preprint [Link]
  • Löffler MT et al. Opportunistic osteoporosis screening reveals low bone density in patients with screw loosening after lumbar semi-rigid instrumentation: a case-control study
  • Löffler MT et al. A Vertebral Segmentation Dataset with Fracture Grading
    Radiology: Artificial Intelligence, 2020. [DOI]
  • Sollmann N et al. Low-dose MDCT: evaluation of the impact of systematic tube current reduction and sparse sampling on the detection of degenerative spine diseases.
    Eur Radiol. 2020 Sep 18 [DOI]
  • Roski F et al. Opportunistic osteoporosis screening: contrast-enhanced dual-layer spectral CT provides accurate measurements of vertebral bone mineral density.
    Eur Radiol. 2020 Oct 14. [DOI]
  • Sollmann N et al. Magnetic resonance neurography of the lumbosacral plexus at 3 Tesla - CSF-suppressed imaging with submillimeter resolution by a three-dimensional turbo spin echo sequence.
    Magn Reson Imaging. 2020 Sep. [DOI]
  • Sollmann N et al. Regional variation in paraspinal muscle composition using chemical shift encoding-based water-fat MRI.
    Quant Imaging Med Surg. 2020 Feb. [DOI]
  • Anitha DP et al. Effect of the intervertebral disc on vertebral bone strength prediction: a finite-element study.
    Spine J. 2020 Apr. [DOI]
  • Löffler MT et al. X-ray-based quantitative osteoporosis imaging at the spine.
    Osteoporos Int. 2020 Feb. [DOI]
  • Sekuboyina A et al. Labelling Vertebrae with 2D Reformations of Multidetector CT Images: An Adversarial Approach for Incorporating Prior Knowledge of Spine Anatomy.
    Radiology: Artificial Intelligence, 2020. [DOI]
  • Löffler MT et al. Improved prediction of incident vertebral fractures using opportunistic QCT compared to DXA.
    Eur Radiol. 2019 Feb 21. [DOI]
  • Valentinitsch A et al. Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures.
    Osteoporos Int. 2019 Mar 4. [DOI]
  • Sollmann N et al. Multi-detector CT imaging: impact of virtual tube current reduction and sparse sampling on detection of vertebral fractures.
    Eur Radiol. 2019 Mar 22. [DOI]
  • Burian E et al. 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]
  • Klupp E et al. Paraspinal Muscle DTI Metrics Predict Muscle Strength.
    J Magn Reson Imaging. 2019 Feb 5. [DOI]
  • Baum T et al.. 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. [DOI]
  • Hedderich DM et al. Differentiation of Acute/Subacute versus Old Vertebral Fractures in Multislice Detector Computed Tomography: Is Magnetic Resonance Imaging Always Needed?
    World Neurosurg. 2019 Feb. [DOI]
  • Anitha D et al. 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. [DOI]


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
Kati Nispel
Sebastian Rühling
Michael Dieckmeyer
Egon Burian
Sofiia Pazynych
Cand. Med.
Magaly Gutbrod-Fernandez
Cand. Med.
Isabel Seeger
Cand. Med.
Eva Niederreiter
Cand. Med.


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

jan.kirschke@tum.de   anjany.sekuboyina@tum.de

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.