Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks
This enables faster and scalable body composition analysis for clinical and research use in oncology, though it is incremental as it applies existing neural network methods to a specific medical imaging task.
The paper tackled the problem of automating body composition analysis from CT scans in cancer patients, achieving Dice scores of 0.95-0.98 and correlation coefficients of R=0.99, comparable to human experts.
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.