Brain Tumor Segmentation from MRI Data Using Ensemble Learning and Multi-Atlas
| Autor: | Timea Fulop, Levente Kovács, Agnes Gyorfi, Laszlo Szilagyi, Bela Suranyi |
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| Rok vydání: | 2020 |
| Předmět: |
Binary decision diagram
Atlas (topology) business.industry Computer science education Multi atlas Pattern recognition Image segmentation Ensemble learning Standard deviation 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Segmentation Artificial intelligence business Brain tumor segmentation 030217 neurology & neurosurgery |
| Zdroj: | 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI) |
| DOI: | 10.1109/sami48414.2020.9108752 |
| Popis: | Atlases are frequently employed to assist medical image segmentation with prior information. This paper introduces a multi-atlas architecture that is trained to locally characterize the appearance (average intensity and standard deviation) of normal tissues in various observed and computed data channels of brain MRI records. The multiple atlas is then deployed to enhance the accuracy of an ensemble learning based brain tumor segmentation procedure that uses binary decision trees. The proposed method is validated using the low-grade tumor volumes of the BraTS 2016 train data set. The use of atlases improve the segmentation quality, causing a rise of up to 1.5% in average Dices scores. |
| Databáze: | OpenAIRE |
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