Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/71246
Título: | Hierarchical brain tumour segmentation using extremely randomized trees |
Autor(es): | Pinto, Adriano Pereira, Sérgio Rasteiro, Deolinda Silva, Carlos A. |
Palavras-chave: | Brain tumour Magnetic resonance imaging Image segmentation Hierarchy of classifiers Extremely randomized trees Machine learning |
Data: | 2018 |
Editora: | Elsevier 1 |
Revista: | Pattern Recognition |
Citação: | Pinto, A., Pereira, S., Rasteiro, D., & Silva, C. A. (2018). Hierarchical brain tumour segmentation using extremely randomized trees. Pattern Recognition, 82, 105-117. doi: https://doi.org/10.1016/j.patcog.2018.05.006 |
Resumo(s): | Gliomas are the most common and aggressive primary brain tumours, with a short-life expectancy in their highest grade. Magnetic Resonance Imaging is the most common imaging technique to assess brain tumours. However, performing manual segmentation is a difficult and tedious task, mainly due to the large amount of information to be analysed. Therefore, there is a need for automatic and robust segmentation methods. We propose an automatic hierarchical brain tumour segmentation pipeline using Extremely Randomized Trees with appearance- and context-based features. Some of these features are computed over non-linear transformations of the Magnetic Resonance Imaging images. Our proposal was evaluated using the publicly available 2013 Brain Tumour Segmentation Challenge database, BRATS 2013. In the Challenge dataset, the proposed approach obtained a Dice Similarity Coefficient of 0.85, 0.79, and 0.75 for the complete, core, and enhancing regions, respectively. |
Tipo: | Artigo |
Descrição: | Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.patcog.2018.05.006 . |
URI: | https://hdl.handle.net/1822/71246 |
DOI: | 10.1016/j.patcog.2018.05.006 |
ISSN: | 0031-3203 |
Versão da editora: | https://www.sciencedirect.com/science/article/pii/S0031320318301699 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
Aparece nas coleções: | CMEMS - Artigos em revistas internacionais/Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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Pinto_Pereira_Silva@2018.pdf Acesso restrito! | 2,28 MB | Adobe PDF | Ver/Abrir |