학술논문
Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks
Document Type
Working Paper
Author
Bridge, Christopher P.; Rosenthal, Michael; Wright, Bradley; Kotecha, Gopal; Fintelmann, Florian; Troschel, Fabian; Miskin, Nityanand; Desai, Khanant; Wrobel, William; Babic, Ana; Khalaf, Natalia; Brais, Lauren; Welch, Marisa; Zellers, Caitlin; Tenenholtz, Neil; Michalski, Mark; Wolpin, Brian; Andriole, Katherine
Source
Subject
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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.