Luca Francesco Rossi
Deep neural networks for brain tumor segmentation in magnetic resonance imaging.
Rel. Lia Morra, Fabrizio Lamberti, Luca Mainardi. Politecnico di Torino, Master of science program in Data Science And Engineering, 2022
|
Preview |
PDF (Tesi_di_laurea)
- Thesis
Licence: Creative Commons Attribution Non-commercial No Derivatives. Download (45MB) | Preview |
|
|
Archive (ZIP) (Documenti_allegati)
- Other
Licence: Creative Commons Attribution Non-commercial No Derivatives. Download (784kB) |
Abstract
Gliomas account for approximately 30% of all brain and central nervous system tumors, and 80% of all malignant brain tumors. Glioblastoma multiforme (GBM) are the most commonly encountered and aggressive malignant primary tumor (15%) of the central nervous system in adults, accounting for approximately 55% of all gliomas. Nowadays, the standard of care treatment for GBM patients consists in surgical resection followed by radiotherapy and chemotherapy, leaving then the subject untreated for the succeeding four months. Deep Learning (DL) has achieved cutting edge results in several medical fields, with its applicability ranging from lesion segmentation to disease relapse prediction. Neuro-oncology, being one of those, has seen important advancements, especially in automating neuroradiology tasks such as brain tumor detection and segmentation.
However, even if state-of-the-art results have been achieved by DL methods on brain tumor segmentation on pre-operative Magnetic Resonance Imaging (MRI) scans, hardly the same can be said of post-operative segmentation, where literature lacks of a more comprehensive study and the few proposed models still present strongly sub-human performances
Relators
Academic year
Publication type
Number of Pages
Course of studies
Classe di laurea
URI
![]() |
Modify record (reserved for operators) |
