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Brain Tumor Segmentation from Multi-Spectral MRI Data Using Cascaded Ensemble Learning

Fulop, Timea and Gyorfi, Agnes and Csaholczi, Szabolcs and Kovacs, Levente and Szilágyi, László (2020) Brain Tumor Segmentation from Multi-Spectral MRI Data Using Cascaded Ensemble Learning. In: 15th IEEE International Conference of System of Systems Engineering (SoSE 2020), 2-4 Jun 2020, Budapest, Magyarország.

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Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Image segmentation, brain tumor segmentation, magnetic resonance imaging, ensemble learning.
Subjects: Q Science / természettudomány > QA Mathematics / matematika > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Q Science / természettudomány > QA Mathematics / matematika > QA76 Computer software / programozás
Depositing User: Dr. László Szilágyi
Date Deposited: 24 Sep 2020 06:18
Last Modified: 24 Sep 2020 06:18
URI: http://real.mtak.hu/id/eprint/114322

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