This repository provides instructions for the body composition assessment tool described in paper "AI Body Composition in Lung Cancer Screening: Added Value Beyond Lung Cancer Detection" published in Radiology (2023).
- See QUICK_START.md for instructions of using the Docker image.
The repository has been updated since the acceptance of the paper. The original repository can be access via commit b2ab4db9cf17a9ebe4b13b195491341ac687636b (Oct 11, 2022, docker image version v1.0.2)
Body composition analysis, which captures the physical and constitutional characteristics of the human body, can provide valuable predictive information for various health outcomes. In this work, we developed a fully automatic pipeline to derive body composition measurements from routine lung screening chest low-dose computed tomography (LDCT). To overcome the systematic field-of-view (FOV) limitations that causing body tissue truncation, we proposed a two-stage method to extend the image border and generate anatomically consistent body tissues in the truncated regions. See our manuscript published in Medical Image Analysis (2023) for more details.
An overview of the framework:
A typical result report obtained for a lung cancer screening CT:
If you find this study can help your work, please consider to cite the following papers.
The body composition measurements improved the prediction for lung cancer death, CVD death, and all-cause mortality in the CT arm of the National Lung Screening Trial (NLST):
Kaiwen Xu, Mirza S. Khan, Thomas Li, Riqiang Gao, James G. Terry, Yuankai Huo, John Jeffrey Carr, Fabien Maldonado, Bennett A. Landman, Kim L. Sandler (2023). AI Body Composition in Lung Cancer Screening: Added Value Beyond Lung Cancer Detection. Radiology, 308(1). https://doi.org/10.1148/radiol.222937
Kaiwen Xu, Thomas Li, Mirza S. Khan, Riqiang Gao, Sanja L. Antic, Yuankai Huo, Kim L. Sandler, Fabien Maldonado, Bennett A. Landman. (2023). Body composition assessment with limited field-of-view computed tomography: A semantic image extension perspective. Medical Image Analysis, 88, 102852. https://doi.org/10.1016/j.media.2023.102852 [arxiv version]
An early version of the pipeline, with automatic slice level selection and body composition segmentation:
Kaiwen Xu, Riqiang Gao, Yucheng Tang, Steve A. Deppen, Kim L. Sandler, Michael N. Kammer, Sanja L. Antic, Fabien Maldonado, Yuankai Huo, Mirza S. Khan, Bennett A. Landman, "Extending the value of routine lung screening CT with quantitative body composition assessment," Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120321L (4 April 2022); https://doi.org/10.1117/12.2611784 [PubMed version]
This project is under the CC BY-NC 4.0 license. See LICENSE.md for more details.
The code and data of this repository are provided to promote reproducible research. The software is provided "as is", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall the authors or copyright holders be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the software or the use or other dealings in the software.