Computer Assisted Medical Interventions

Division of Computer Assisted Medical Interventions

Prof. Dr. Lena Maier-Hein

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The mission of the division of Computer Assisted Medical Interventions is to improve the quality of interventional healthcare in a data-driven manner. To this end, the multidisciplinary group builds upon principles and knowledge from a diversity of research fields including artificial intelligence (AI), statistics, computer vision, biophotonics and medicine. Surgical Data Science, the scientific discipline of enhancing interventional healthcare through capturing, organization, analysis and modeling of data, constitutes the first and central pillar of our research. Committed to the ultimate goal of creating benefit for patients, medical staff and other stakeholders, we place a particular focus on addressing common roadblocks to clinical translation of surgical data science methods such as data sparsity or questions of uncertainty handling. A second pillar is our research in novel interventional imaging techniques enabled by deep learning. We are particularly interested in developing spectral imaging devices into safe and reliable real-time tissue imaging and navigation modalities during interventions. This is achieved with physics-based machine learning concepts that leverage prior knowledge in the form of physical simulations. The division’s profile is complemented by research on the cross-cutting topic of the reliable validation of AI algorithms.

Contact

Prof. Dr. Lena Maier-Hein
Computer Assisted Medical Interventions (E130)
Deutsches Krebsforschungszentrum
Im Neuenheimer Feld 223
69120 Heidelberg
Tel: +49 6221 42 2354

Selected Publications

  • Maier-Hein L, Vedula SS, Speidel S, Navab N, Kikinis R, Park A, Eisenmann M, Feussner H, Forestier G, Giannarou S, Hashizume M. Surgical data science for next-generation interventions. Nature Biomedical Engineering. 2017;1(9):691.
  • Wirkert SJ, Vemuri AS, Kenngott HG, Moccia S, Götz M, Mayer BF, Maier-Hein KH, Elson DS, Maier-Hein L. Physiological Parameter Estimation from Multispectral Images Unleashed. In International Conference on Medical Image Computing and Computer-Assisted Intervention. 2017; LNCS, vol. 10435:134-141.
  • Franz AM, Seitel A, Bopp N, Erbelding C, Cheray D, Delorme S, Grünwald F, Korkusuz H, Maier-Hein L. First clinical use of the EchoTrack guidance approach for radiofrequency ablation of thyroid gland nodules. International Journal of Computer Assisted Radiology and Surgery. 2017; 12(6):931-40
  • Maier-Hein L, Franz AM, dos Santos TR, Schmidt M, Fangerau M, Meinzer HP, Fitzpatrick JM. Convergent iterative closest-point algorithm to accomodate anisotropic and inhomogenous localization error. Pattern Analysis and Machine Intelligence, IEEE Transactions on. 2012;34(8):1520-32.
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