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MA: Tissue Recognition from Contrast Enhanced Sonography Time Series

left: contrast enhanced time series; right: normal ultrasound image

In the medical domain, visualizing the interior of our body is crucial for detecting structures and diseases hidden by our skin. Such visualizations are created by advanced technology like MRI, CT, Ultra Sound (Sonography), which capture fine granular information in millimetre sizes nowadays. As MRI and CT images are cost intensive, Ultra Sound imaging can be performed as a cheaper solution.

Contrast Enhanced Ultra Sound is a novel technique, which can be used for follow-up diagnostics in a convenient way. The imaging gives the physician insights about the type of tissue, which might by a tumour or not.


In this thesis, the following tasks are included:

  • Import real world patient data as DICOM format into the analysis framework
  • Testing best practices algorithms for tissue recognition, including hot topics like Deeplearning
  • Create visualizations for the algorithmic results (videos, static images and helpful diagrams)
  • Evaluating the results with a research team at the University Hospital Münster

Prerequisites for working on this topic are advanced knowledge in algorithmic design and good programming skills in either Java or Phython.

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Florian Schmidt
+49 30 314 28306
Room TEL 1205