A comparison study of U-Net based methods for brain tumor segmentation
The brain tumor segmentation (BraTS) Challenge is an international competition that focuses on the task of automated segmentation of the different parts of brain tumors in magnetic resonance imaging (MRI) scans. U-Net architecture has become the de-facto standard for medical image segmentation tasks...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/407795 |
| Acceso en línea: | https://hdl.handle.net/2117/407795 |
| Access Level: | acceso abierto |
| Palabra clave: | Imaging systems in medicine Machine learning Deep learning (Machine learning) Image segmentation BraTS-Africa U-Net Swin Transformer Swin UNETR BraTS challenge MRI scans aprenentatge automàtic aprenentatge profund aprenentatge per transferència imatges biomèdiques segmentació d'imatges mèdiques segmentació de tumors cerebrals concurs BraTS ressonància magnètica glioma adult meningioma intracranial metàstasis cerebrals tumors pediàtrics machine learning deep learning transfer learning biomedical images medical image segmentation brain tumor segmentation adult glioma intracranial meningioma brain metastases pediatric tumors Imatgeria mèdica Aprenentatge automàtic Aprenentatge profund Imatges--Segmentació Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | The brain tumor segmentation (BraTS) Challenge is an international competition that focuses on the task of automated segmentation of the different parts of brain tumors in magnetic resonance imaging (MRI) scans. U-Net architecture has become the de-facto standard for medical image segmentation tasks, and the proposals based on this architecture have been among the top-ranked solution proposals in the last editions. The 2023 edition of the BraTS challenge introduced a set of 4 new datasets towards addressing additional populations (e.g., sub-Saharan Africa patients) and types of tumors (e.g., meningioma). The goal of this thesis is to train and test different U-Net based architectures using the datasets of the 2023 edition, and to compare and analyse the performance of the different methods both quantitatively and qualitatively. |
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