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Automatic Brain Tissue Segmentation of Multi-sequence MR Images Using Random Decision Forests
|Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/3444|
Published in The MIDAS Journal - The MICCAI Grand Challenge on MR Brain Image Segmentation (MRBrainS13) .
Submitted by Sérgio Pereira on 10-23-2013.
This work is integrated in the MICCAI Grand Challenge: MR Brain Image Segmentation 2013. It aims for the automatic segmentation of brain into Cerebrospinal fluid (CSF), Gray matter (GM) and White matter (WM). The provided dataset contains patients with white matter lesions, which makes the segmentation task more challenging. The proposed algorithm uses multi-sequence MR images to extract meaningful features and learn a Random Decision Forest that classifies each voxel of the image. The results show that it is robust to the presence of the white matter lesions, and the metrics show that the overall results are competitive.
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|Categories:||Classification, Decision trees and non-metric classification, Segmentation|
|Keywords:||Random decision forests, Brain tissue segmentation, multi-sequence, MRI|
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