Seminar Intraoperative Imaging and Machine Learning



Time and place:

This course will be held online until further notice. Please register via in StudOn starting from March 15, 2021. Note that equal chances for all applicants apply until March 27, midnight.

  • Wed 8:30-10:00, Room 09.150

Fields of study

  • WPF MT-MA-BDV from SEM 1
  • WPF INF-MA from SEM 1
  • WPF ICT-MA from SEM 1
  • WPF CE-MA-INF from SEM 1


For many applications, techniques like deep learning allow for considerably faster algorithm development and allow to automate tasks that were performed manually in the past. In medical imaging, a large variety of time-consuming tasks that interfere with clinical workflows has the potential for automation. However, at the same time new challenges arise like data privacy regulations and ethics concerns.
In this seminar, we want to develop an application that allows for the automation of an X-ray based intraoperative planning or measurement procedure from a holistic perspective. To this end, we will invite a surgeon to explain the medical background and visit the operating room to understand the surgeons’ needs while performing the task. Having understood the underlying medical problem, we will look into topics of data privacy, code of ethics, prototype development, and UI design for surgeons. Furthermore, we will touch regulatory requirements necessary for releasing software to clinics.
At the end of the seminar, the students will have developed and documented a prototypical application for the indented intraoperative use case.
Students will be able to

  • visit an operation room, following the rules of such an environment

  • perform their own literature research on a given subject

  • independently research this subject according to data privacy and ethical standard

  • present and introduce the subject to their student peers

  • give a scientific talk in English according to international conference standards

  • describe their results in a scientific report

Additional information

Expected participants: 6