Semester work

The task is to implement a simple image processing algorithm with medical imaging applications or apply it to some (bio)medical imaging task. It should take you about 10 hours, so it should not be completely trivial.

You are encouraged to choose your own task; otherwise it will be assign to you by the instructors. In both cases, the students are expected to write a short description of their task, and send it to their lab instructor for approval. The tasks must be proposed and approved by November 8 (7th week)November 15 (8th week).

The implementation must be yours, written from scratch. You are not allowed to use any existing code, or trivial modifications thereof. Such attempts will be considered a plagiarism and will result in a failure from the course. Libraries can be used for supporting tasks such as the deep learning infrastructure, numerical methods or classifiers, if they are not the focus of the project. Library functions may be used for common tasks (e.g. reading or displaying image, solving systems of linear equations etc.). If in doubt, check with the instructor.

The required output of the project is the implementation itself and a short report, describing the task in the context of the state of the art methods, the method and its implementation and how to use it, and the experimental results.

The report and the code must be uploaded as one archive to BRUTE as a solution of the assignment titled Semester work by January 8, 2024 (i.e., until January 8, 23:59:59). The report is expected to be about 3-5 pages.

The students will present their works the last week of the semester on January 10, 2024. Each presentation is expected to be 5-8 minutes long to allow a short discussion after.

The project might involve, for example:

• active contour segmentation based on edge terms
• active contour segmentation based on intensity (Chan-Vese)
• gradient vector flow segmentation
• level set segmentation (2D is enough)
• segmentation with active shape and appearance models (synthetic data is sufficient)
• finding superpixels (e.g. SLIC)
• superpixel segmentation (superpixels can be found using an existing library)
• normalized cut segmentation
• GraphCut segmentation - implement the optimization yourself
• random walker segmentation
• texture classification using wavelet descriptors
• texture classification using textons
• U-net segmentation - study the dependence on parameters and architecture
• anatomically constrained neural networks - test a simplified case on synthetic data
• cell nuclei detection using simplified Al-Kofahi's method
• cell nuclei detection using simplified deep regression
• implement Frangi's vesselness, generalize and optimize parameters
• deep regression based retina vessel segmentation (e.g. IOSTAR dataset)

You can find some datasets at these links:

• A list of many medical datasets by Dr. Rivaz
• SCR
• BrainWeb
• PatchCamelyon
• Medical Segmentation Decathlon