|Topic:||Automatické segmentace lézí roztroušené sklerózy ve snímcích magnetické rezonance pomocí hlubokého učení|
|Supervisor:||Ing. Milan Němý|
|Announce as:||Diplomová práce, Bakalářská práce, Semestrální projekt|
|Description:||Magnetic resonance imaging (MRI) represents one of the most important tools for diagnosing multiple sclerosis, monitoring the disease progression, and predicting its future development. One of the leading indicators of disease development is the number, location, and volume of brain lesions caused by the demyelinating process. Traditionally, T2-weighted sequences and FLAIR imaging are used to detect these hyperintense lesions. However, a variety of new MR imaging techniques are currently beginning to be used to increase detection sensitivity and provide a more comprehensive view of central nervous system damage. In this project, the student will create an overview of MR imaging techniques for the segmentation of brain lesions in multiple sclerosis. Furthermore, the student will implement a selected segmentation procedure using a deep learning approach and will compare their results with a gold standard. Contact email: email@example.com|
|Bibliography:||1. Commowick, Olivier, et al. "Objective evaluation of multiple sclerosis lesion segmentation using a data management and processing infrastructure." Scientific reports 8.1 (2018): 1-17.
2. Commowick, Olivier, Frédéric Cervenansky, and Roxana Ameli. "MSSEG challenge proceedings: multiple sclerosis lesions segmentation challenge using a data management and processing infrastructure." Miccai. 2016.
3. Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-net: Convolutional networks for biomedical image segmentation." International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015.