Medical Image Processing 2 - Interactive curriculae of TU Ilmenau

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module properties module number 201264 - common information
module number	201264
department	Department of Computer Science and Automation
ID of group	2226
module leader	Prof. Dr. Sylvia Saalfeld
language	Englisch
term	Sommersemester
previous knowledge and experience	Basics in Image Processing
learning outcome	The students are able to understand the presented segmentation techniques and explain them using the example of 2D, 3D, and 4D medical data. This competency is assessed with the second part of the course, in which the participants have to solve practical exercises and explain them to the other participants. The students are capable of implementing segmentation techniques, i.e., applying the techniques presented in the course to real life medical image data. The students are able to evaluate the performance of the segmentations and thus assess which segmentation technique is best suitable with respect to duration time and accuracy. The students possess the necessary prerequisite knowledge to identify suitable segmentation techniques for novel applications.
content	The most important medical segmentations are carried out for specific medical application scenarios (e.g. intracranial aneurysms, liver cancer data, ...). Building on the basics in Image processing, the basic techniques will be expanded in order to implement image processing methods for specific medical data sets. These include region-based methods (Thresholding, Region Growing, Markov Random Fields, Watershed, Graph Cuts), Deformable Models (Active Contour, Level-Set Methods), Atlas-Guided Methods, and Machine Learning Methods (Supervised and Unsupervised Methods, Deep Learning Methods). The medical data sets are made available through collaborations with clinical partners or through freely available challenge data (e.g. http://medicaldecathlon.com/). Finally, evaluation metrics (e.g. DICE score, F1 score) are used to assess the segmentation quality of the implemented techniques.
media of instruction and technical requirements for education and examination in case of online participation	Tafelmitschriften, Folien, Computer-basierte Präsentation, Demonstration , Übungsaufgaben, Software
literature / references	Computer Vision: Algorithms and Applications, 2nd Edition, Richard Szeliski, 2021 Alzahrani, Y., & Boufama, B. (2021). Biomedical image segmentation: a survey. SN Computer Science, 2, 1-22. In addition, a list of references with scientific papers will be provided at the end of each lecture specific for each topic
evaluation of teaching

Details reference subject
module name	Medical Image Processing 2
examination number	220502
credit points	5
SWS	4 (2 V, 2 Ü, 0 P)
on-campus program (h)	45
self-study (h)	105
obligation	obligatory module
exam	examination performance with multiple performances
details of the certificate	Das Modul Medical Image Processing 2 mit der Prüfungsnummer 220502 schließt mit folgenden Leistungen ab: schriftliche Prüfungsleistung über 90 Minuten mit einer Wichtung von 100% (Prüfungsnummer: 2200886) alternative semesterbegleitende Studienleistung mit einer Wichtung von 0% (Prüfungsnummer: 2200887) Details zum Abschluss Teilleistung 2: 1/2 of the set seminar tasks must be solved and, if necessary, explained to the other participants A documented instruction is required each semester in order to carry out laboratory experiments.
link to Moodle course
teacher	Prof, Saalfeld
signup details for alternative examinations
maximum number of participants

Details in degree program Master Biomedical Engineering by Research 2025, Master Biomedical Engineering by Research 2026
module name	Medical Image Processing 2
examination number	220502
credit points	5
on-campus program (h)	45
self-study (h)	105
obligation	obligatory module
exam	examination performance with multiple performances
details of the certificate	Das Modul Medical Image Processing 2 mit der Prüfungsnummer 220502 schließt mit folgenden Leistungen ab: schriftliche Prüfungsleistung über 90 Minuten mit einer Wichtung von 100% (Prüfungsnummer: 2200886) alternative semesterbegleitende Studienleistung mit einer Wichtung von 0% (Prüfungsnummer: 2200887) Details zum Abschluss Teilleistung 2: 1/2 of the set seminar tasks must be solved and, if necessary, explained to the other participants A documented instruction is required each semester in order to carry out laboratory experiments.
link to Moodle course
signup details for alternative examinations
maximum number of participants