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PATIENT RISK STRATIFICATION BASED ON BODY COMPOSITION DERIVED FROM COMPUTED TOMOGRAPHY IMAGES USING MACHINE LEARNING
PATIENT RISK STRATIFICATION BASED ON BODY COMPOSITION DERIVED FROM COMPUTED TOMOGRAPHY IMAGES USING MACHINE LEARNING
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机译:基于机器学习的计算机断层图像图像衍生的身体成分的患者风险分层
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摘要
A system and method for determining patient risk stratification is provided based on body composition derived from computed tomography images using segmentation with machine learning. The system may enable real-time segmentation for facilitating clinical application of body morphological analysis sets. A fully-automated deep learning system may be used for the segmentation of skeletal muscle cross sectional area (CSA). Whole-body volumetric analysis may also be performed. The fully-automated deep segmentation model may be derived from an extended implementation of a Fully Convolutional Network with weight initialization of a pre-trained model, followed by post processing to eliminate intramuscular fat for a more accurate analysis.
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