Research at the Division of Computer Assisted Medical Interventions

Augmented Reality

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Key challenges in interventional tumor diagnosis and therapy consist of the detection and discrimination of malignant tissue as well as the precise navigation of medical instruments. Currently, a low level of sensitivity and specificity in tumor detection and lack of global orientation lead to both over- and undertreatment, tumor recurrence, intra-operative complications, and high costs. Our goal is to revolutionize clinical interventional imaging based on novel image acquisition and analysis methods. Our methodology presents a confluence of biophotonics techniques (multispectral optical and optoacoustic imaging) and machine learning techniques and allows for Augmented Reality visualization of a range of important morphological and functional parameters such as blood oxygenation. Current projects are:

 

Surgical Data Science

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Our vision is to observe everything occurring within and around the treatment process in order to provide context-aware assistance to the physicians. This will be achieved by holistic analysis of all relevant data in the course of disease treatment. Dynamically acquired factual and practical knowledge (e.g. study results, clinical outcomes in similar cases) as well as individual patient data (e.g. laboratory data, pre-operative images, intra-operative sensor data) shall be continuously accumulated and processed to support the physician’s decisions and actions in a knowledge-based manner. Initial steps towards implementing this vision have been taken in two projects:

 

Clinical Translation

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To pave the way for clinical translation of our work, all of our projects are conducted in close collaboration with clinical collaborators, mainly from the University Clinic Heidelberg. Furthermore, translational funding was recently acquired for innovative solutions in the fields of interventional radiology and surgery:

 

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

  • 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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