Brain Tumor Segmentation from Multi-Spectral MRI Data Using Cascaded Ensemble Learning *
| Autor: | Timea Fulop, Levente Kovács, Szabolcs Csaholczi, Laszlo Szilagyi, Agnes Gyorfi |
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| Rok vydání: | 2020 |
| Předmět: |
Pixel
Computer science Binary decision diagram business.industry Physics::Medical Physics Pattern recognition Image segmentation computer.software_genre Ensemble learning 030218 nuclear medicine & medical imaging Random forest Correlation 03 medical and health sciences 0302 clinical medicine Voxel Segmentation Artificial intelligence business computer 030217 neurology & neurosurgery |
| Zdroj: | 2020 IEEE 15th International Conference of System of Systems Engineering (SoSE) SoSE |
| DOI: | 10.1109/sose50414.2020.9130550 |
| Popis: | Ensemble learning methods are frequently employed in medical decision support. In image segmentation problems the ensemble based decisions require a postprocessing, because the ensemble cannot adequately handle the strong correlation of neighbor voxels. This paper proposes a brain tumor segmentation procedure based on an ensemble cascade. The first ensemble consisting of binary decision trees is trained to separate focal lesions from normal tissues based on four observed and 100 computed features. Starting from the intermediary labels provided by the first ensemble, six local features are computed for each voxel that serve as input for the second ensemble. The second ensemble is a classical random forest that enforces the correlation between neighbor pixels, regularizes the shape of the lesions. The segmentation accuracy is characterized by 85.5% overall Dice Score, 0.5% above previous solutions. |
| Databáze: | OpenAIRE |
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