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• W3102509263 abstract "The amounts of muscle and fat in a person’s body, known as body composition, are correlated with cancer risks, cancer survival, and cardiovascular risk. The current gold standard for measuring body composition requires time-consuming manual segmentation of CT images by an expert reader. In this work, we describe a two-step process to fully automate the analysis of CT body composition using a DenseNet to select the CT slice and U-Net to perform segmentation. We train and test our methods on independent cohorts. Our results show Dice scores (0.95−0.98) and correlation coefficients (R = 0.99) that are favorable compared to human readers. These results suggest that fully automated body composition analysis is feasible, which could enable both clinical use and large-scale population studies." @default.
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• W3102509263 date "2018-01-01" @default.
• W3102509263 modified "2023-10-16" @default.
• W3102509263 title "Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks" @default.
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• W3102509263 doi "https://doi.org/10.1007/978-3-030-01201-4_22" @default.
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