Awesome

CT Body Composition

NOTE: As of May 2024, this project is now archived in favor of this updated version that now includes model weights.

This repository provides code for training and running body composition estimation models on abdominal CT scans.

Getting Started

After cloning the repository, you have two options for setting up the environment. You may install all the necessary components directly on your system, or if you have docker on your machine, you may build a docker image that contains all the necessary requirements.

See the documentation pages for further details:

Installation - For installing directly on your system
Docker - For building and using the docker image
Training - For training new models
Inference - For running the model on new data

Model Weights

At this stage, we are not releasing our trained model weights publicly on github, and you will not find them in this repository. You are welcome to use this code on your own data to develop your own model, and you will find full instructions on how to do so in the documentation. We are however happy to discuss collaboration possibilities with investigators interested in using our models (including the deep learning models and population curve models) for their own studies. Please email us to discuss further:

Chris Bridge, Massachusetts General Hospital (cbridge at partners dot org)
Kirti Magudia, Duke University (kirti dot magudia at duke dot edu)
Michael Rosenthal, Dana Farber Cancer Institute (Michael underscore Rosenthal at dfci dot harvard dot edu)
Florian Fintelmann, Massachusetts General Hospital (fintelmann at mgh dot harvard dot edu)
Camden Bay, Brigham and Women's Hospital (cpbay at bwh dot harvard dot edu)

Publications

This code accompanies the following publication:

Population-Scale CT-Based Body Composition Analysis Of a Large Outpatient Population Using Deep Learning To Derive Age, Sex, and Race-Specific Reference Curves

K. Magudia, C.P. Bridge, C.P. Bay, A. Babic, F.J. Fintelmann, F. Troschel, N. Miskin, W. Wrobel, L.K. Brais, K.P. Andriole, B.M. Wolpin, and M.H. Rosenthal

Radiology (In Press)

Article at RSNA

Furthermore, an earlier version of the same model was developed for the following publication:

Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks

C.P. Bridge, M. Rosenthal, B. Wright, G. Kotecha, F. Fintelmann, F. Troschel, N. Miskin, K. Desai, W. Wrobel, A. Babic, N. Khalaf, L. Brais, M. Welch, C. Zellers, N. Tenenholtz, M. Michalski, B. Wolpin, and K. Andriole

Workshop on Clinical Image-based Procedures, MICCAI, Granada 2018

Article at Springer Link, Article at Arvix

If you use this code in your publication, please cite these papers.

Acknowledgements