Detail of the student project

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Topic:Klasické metody automatické segmentace lézí roztroušené sklerózy ve snímcích magnetické rezonance
Department:Katedra kybernetiky
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 selected classical segmentation procedures and will compare their accuracy with available segmentation tools. Contact email: milan.nemy@cvut.cz
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. Schmidt, Paul, et al. "An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis." Neuroimage 59.4 (2012): 3774-3783.
Responsible person: Petr Pošík