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Research at the Division of Intelligent Medical Systems

The mission of our division is to improve the quality of interventional healthcare in a data-driven manner. To this end, our multidisciplinary group builds upon principles and knowledge from a diversity of research fields including artificial intelligence (AI), statistics, computer vision, biophotonics and medicine. Committed to the ultimate goal of creating benefit for patients and medical staff, we aim to develop a holistic concept spanning the three significant topics perception, data interpretation and real-time assistance, and connecting them through a cycle of continuous learning: Novel spectral imaging techniques enabled by deep learning are being developed as safe, reliable and real-time imaging modalities during interventions. When interpreting the perceived data in the context of available knowledge, our division specifically addresses common roadblocks to clinical translation such as data sparsity, explainability and uncertainty handling. In close collaboration with clinical partners, these methods are leveraged for the development of context-aware interventional assistance systems. Finally, we place a strong focus on the reliable validation of AI algorithms for clinical purposes.

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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 directly 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:
 

Spectral Imaging

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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/initiatives are:

Validation

Every year, hundreds of new algorithms are published in the field of biomedical image analysis. For a long time, validation and evaluation of new methods was based on the authors' personal data sets, rendering fair and direct comparison of solutions impossible. In the meantime, common research practice has changed and involves organization of international competitions ('challenges') that allow for benchmarking algorithms on publicly released data sets. While this was a great step forward, we have observed a critical discrepancy between the current impact of challenges and their quality. We are therefore collaborating with a number of institutes world-wide to bring biomedical image analysis challenges and the validation of ML algorithms in general to the next level of quality. Current projects/initiatives include:

Selected publications

  • Groehl J, Kircher, T, Adler, TJ, Hacker, L, Holzwarth, N, Hernándet-Aguilera, A, Herrera, MA, Santos, E, Bohndiek, SE, Maier-Hein L. Learned spectral decoloring enables photoacoustic oximetry. Nature Scientific Reports. 2021 11(6565).
  • Ardizzone, L., Kruse, J., Wirkert, S., Rahner, D., Pellegrini, E. W., Klessen, R. S., Maier-Hein, L., Rother, C., & Köthe, U. (2019). Analyzing Inverse Problems with Invertible Neural Networks. International Conference on Learning Representations. https://openreview.net/forum?id=rJed6j0cKX 
  • Maier-Hein, L, Eisenmann, M, Reinke, A, Onogur, S, Stankovic, M, Scholz, P, Arbel, T, Bogunovic, H, Bradley, AP, Carass, A, Feldmann, C, Frangi, AF, Full, PM, van Ginneken, B, Hanbury, A, Honauer, K, Kozubek, M, Landman, BA, März, K, ... Kopp-Schneider, A. Why rankings of biomedical image analysis competitions should be interpreted with care. Nature Communications. 2018; 9(1):5217.
  • 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.

[more]

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"

[more]

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