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• W1995873471 abstract "Glioblastoma Mulitforme is highly infiltrative, making precise delineation of tumor margin difficult. Multimodality or multi-parametric MR imaging sequences promise an advantage over anatomic sequences such as post contrast enhancement as methods for determining the spatial extent of tumor involvement. In considering multi-parametric imaging sequences however, manual image segmentation and classification is time-consuming and prone to error. As a preliminary step toward integration of multi-parametric imaging into clinical assessments of primary brain tumors, we propose a machine-learning based multi-parametric approach that uses radiologist generated labels to train a classifier that is able to classify tissue on a voxel-wise basis and automatically generate a tumor segmentation. A random forests classifier was trained using a leave-one-out experimental paradigm. A simple linear classifier was also trained for comparison. The random forests classifier accurately predicted radiologist generated segmentations and tumor extent." @default.
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• W1995873471 date "2013-07-01" @default.
• W1995873471 modified "2023-09-27" @default.
• W1995873471 title "Predicting a multi-parametric probability map of active tumor extent using random forests" @default.
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• W1995873471 doi "https://doi.org/10.1109/embc.2013.6611038" @default.
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