Computer Assisted Medical Interventions

Surgical data science is an emerging scientific discipline with the objective of improving the safety, quality, effectiveness, and efficiency of patient care. The ultimate goal is to support physicians throughout the entire process of disease diagnosis, therapy and follow-up with the right information at the right time.

To achieve this, we propose integration of the research fields of machine learning, medical image processing, semantic modelling and biophotonics. Our focus is on technological innovation with a strong emphasis on clinical translation and direct patient benefits.

Current research topics include computational multispectral optical and photoacoustic imaging in cancer diagnosis and therapy, real-time fusion of multi-modal patient data in the presence of motion, holistic data processing for decision support as well as workflow optimized approaches to computer guidance.

In conventional endoscopy, the physician relies on streams of white-light images to detect and locate malignant tissue, vital structures, as well as medical instruments. We propose to holistically process multimodal structural and functional information obtained from pre-operative data co-registered to optical and photoacoustic interventional data for both (1) highly specific local tissue classification and discrimination as well as (2) global context-aware instrument guidance. This approach is currently being implemented and evaluated by means of computer-assisted colonoscopy and laparoscopy.
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Research topics

Selected publications

  • Wirkert SJ, Kenngott H, Mayer B, Mietkowski P, Wagner M, Sauer P, Clancy NT, Elson DS, Maier-Hein L. Robust near real-time estimation of physiological parameters from megapixel multispectral images with inverse Monte Carlo and random forest regression. International Journal of Computer Assisted Radiology and Surgery, 1-9, 2016.

  • März K, Hafezi M, Weller T, Saffari A, Nolden M, Fard N, Majlesara A, Zelzer S, Maleshkova M, Volovyk M, Gharabaghi N, Wagner M, Emami G, Engelhardt S, Fetzer A, Kenngott H, Rezai N, Rettinger A, Studer R, Mehrabi A, Maier-Hein L. Toward Knowledge-Based Liver Surgery: Holistic Information Processing for Surgical Decision Support. International Journal of Computer Assisted Radiology and Surgery (Special Issue: IPCAI), 1–11. doi:10.1007/s11548-015-1187-0, 7. April 2015.

  • Dos Santos TR, Seitel A, Kilgus T, Suwelack S, Wekerle AL, Kenngott H, Speidel S, Schlemmer HP, Meinzer HP, Heimann T, Maier-Hein L. Pose-independent surface matching for intra-operative soft-tissue marker-less registration. Med Imag Anal(accepted), 2014.

  • Maier-Hein L, Franz AM, dos Santos TR, Schmidt M, Fangerau M, Meinzer HP, Fitzpatric JM. Convergent Iterative closest-point algorithm to accomodate anisotropic and inhomogenous localization error. IEEE T Pattern Anal 34(8):1520-1532, 2012.

  • Maier-Hein L, Tekbas A, Seitel A, Pianka F, Müller SA, Satzl S, Schawo S, Radeleff B, Tetzlaff R, Franz AM, Müller-Stich BP, Wolf I, Kauczor HU, Schmied BM,  Meinzer HP. In-vivo accuracy assessment of a needle-based navigation system for CT-guided radiofrequency ablation of the liver. Med Phys, 35(12):5385-5396, 2008.

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