Research at the Division of Computer Assisted Medical Interventions

The mission of our division is to improve the quality of interventional healthcare computationally. Our vision is to support physicians throughout the entire process of disease diagnosis, therapy and follow-up in a data-driven manner (“the right information to the right person at the right time”). To this end, our multidisciplinary group builds upon principles from a diversity of research fields including machine learning, biophotonics, knowledge modeling and evidence-based medicine. Current activities focus on the topics surgical data science, computational biophotonics and clinical translation.

Surgical Data Science

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Surgical data science aims to improve the quality of interventional healthcare and its value through the capture, organization, analysis and modelling of data. In this paradigm, data may relate to any step of the patient care, may concern the patient, caregivers, as well as technology used to deliver care, and may be analyzed in the context of generic domain-specific knowledge. The unique scientific challenges related to the analysis of data from interventions include those related to speed, robustness as well as the heterogeneity and complexity of the procedures. Methodological research in our unit is therefore related to the joint analysis of procedural data with other heterogeneous data, real-time uncertainty quantification and compensation, as well as efficient data annotation. Current projects are:

 

Computational Biophotonics

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Imaging is a key prerequisite for assistance in interventional healthcare. Currently available intra-operative modalities suffer from radiation exposure to the patient/physician, slow acquisition times, poor discrimination of relevant structure and/or high complexity and costs. Our goal is, therefore, to revolutionize clinical interventional imaging based on novel image acquisition and analysis methods. Our methodology involves recent biophotonics techniques including multispectral optical and optoacoustic imaging as well as modern machine learning techniques and enable Augmented Reality visualization of a range of important morphological and functional parameters such as blood oxygenation. Current projects are:

 

Clinical Translation

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A key cross-topic focus of our unit is to develop innovative approaches that have a high potential for clinical translation.  A strategic priority is therefore to develop the infrastructure, tools and workflow concepts for enabling data-driven surgical oncology. Furthermore, all of our projects are conducted in close collaboration with clinical collaborators and have clinical applications which range from surgery to interventional radiology, endoscopy and even forensic medicine. Current translational projects are:
 

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.

  • Maier‐Hein L, Tekbas A, Seitel A, Pianka F, Müller SA, Satzl S, Schawo S, Radeleff B, Tetzlaff R, Franz AM, Müller‐Stich BP. In vivo accuracy assessment of a needle‐based navigation system for CT‐guided radiofrequency ablation of the liver. Medical Physics. 2008;35(12):5385-96.

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Selected awards

  • Berlin-Brandenburg Academy Prize (2017)
    Lena Maier-Hein for outstanding contributions to the field of cancer research

  • IPCAI Bench to Bedside Award  (2017)
    A. Franz et al., for the paper "First clinical use of the EchoTrack guidance approach for radiofrequency ablation of thyroid gland noules."

  • Philips / IPCAI Audience Best Presentation Award (2015)
    Keno März et al., for the paper "Towards Knowledge-Based Liver Surgery - Holistic Information Processing for Surgical Decision Support"

  • Heinz Maier-Leibnitz Award (2013)
    Lena Maier-Hein, for "Outstanding research"

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