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• W2509099314 abstract "There is a need for robust, fully automated whole body organ segmentation for diagnostic CT. This study investigates and optimizes a Random Forest algorithm for automated organ segmentation; explores the limitations of a Random Forest algorithm applied to the CT environment; and demonstrates segmentation accuracy in a feasibility study of pediatric and adult patients. To the best of our knowledge, this is the first study to investigate a trainable Weka segmentation (TWS) implementation using Random Forest machine-learning as a means to develop a fully automated tissue segmentation tool developed specifically for pediatric and adult examinations in a diagnostic CT environment." @default.
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• W2509099314 date "2016-08-17" @default.
• W2509099314 modified "2023-09-25" @default.
• W2509099314 title "Tissue segmentation of computed tomography images using a Random Forest algorithm: a feasibility study" @default.
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• W2509099314 doi "https://doi.org/10.1088/0031-9155/61/17/6553" @default.
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