BodyComposition v0.1

This package combines model weights of TotalSegmentator with pre- and postprocessing steps to calculate the cross-sectional areas of skeletal muscle (SM_total), psoas muscle (SM_psoas), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT) and intermuscular adipose tissue (IMAT) from routine computed tomographies.

Installation

1. We recommend to install this pipeline in a separate python environment.

2. Navigate to the directory you would like to install the pipeline in.

3. Install the pipeline

   git clone https://github.com/fohofmann/BodyComposition.git
   cd BodyComposition
   pip install --no-cache-dir -e .

4. Make sure the TotalSegmentator model weights are available:

Model weights

Currently, this repository does not provide own model weights. Instead, we combine model weights of TotalSegmentator with pre- and postprocessing steps. BodyComposition v0.1 uses the following task classes from TotalSegmentator: total, body, vertebrae_body, and tissue_types. The tasks total and body are available under CC BY 4.0 license, whereas vertebrae_body and tissue_types require a license available from J. Wasserthal / TotalSegmentator.

• If you are already using TotalSegmentator in your current environment, you can skip the following steps. As we use TotalSegmentator's python API, the pipeline should just work.

• If you have not used Totalsegmentator in your current environment so far, you have to obtain the model weights. To do so, please refer to TotalSegmentator:

  • Obtain a license using the provided form. Please note and respect TotalSegmentator's license conditions, especially regarding the non-commercial use!

  • Set your license: totalseg_set_license -l aca_12345678910.

  • Download the model weights:

    totalseg_download_weights -t total
    totalseg_download_weights -t body
    totalseg_download_weights -t vertebrae_body
    totalseg_download_weights -t tissue_types

• If you have a license and model weights already available in an other environment, you can also provide its localization manually by editing config/general.yaml:

  • weights/totalsegmentator sets the environment variable TOTALSEG_WEIGHTS_PATH = path to the nnUNet_results directory used by TotalSegmentator

  • tseg_config sets the environment variable TOTALSEG_HOME_DIR = path to the home directory of TotalSegmentator, e.g. containing the configuration file 8including your license key)

  This method can also be used to mount a model directory when you are using the provided docker. We can not and do not want to integrate TotalSegmentator's (tissue and vertebral bodies) model weights into the container directly.

Usage

After installation of the pipeline and TotalSegmentator, you can use the pipeline via the command line:

bin/RunBatch -i ./data/images -f '^ct_.*\.nii\.gz$' -m SarcopeniaTotalSegmentator

The following flags are available:

• --input / -i: Path to input, either directory (e.g., data/images) or datalist file (*.json) or single NiFTI file
• --filter / -f: Regex string to filter and subset input files (e.g., '^ct_.*\.nii\.gz$')
• --config / -c: Path to configuration file (*.yaml), or dictionary. Can be used to update the default configuration
• --method / -m: Name of pipeline method to be run, as defined in the pipeline_registry.py. Currently available options are SarcopeniaTotalSegmentator and SarcopeniaTotalSegmentatorFast

bin/RunBatch is just an comand line access point to python_api.py. You can also use this API directly from your scripts. For details, have a look at the file.

More

• A detailed description of the pipeline and it's logic can be found in docs/pipeline.md.
• The labels returned as labels and masks are described in docs/labels.md.
• For more information regarding the config/-files and available options, see docs/config.md

Citations

If you use this code, you cite the following repositories and papers:

nnU-Net

Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.

TotalSegmentator

Wasserthal, J., Breit, H.-C., Meyer, M.T., Pradella, M., Hinck, D., Sauter, A.W., Heye, T., Boll, D., Cyriac, J., Yang, S., Bach, M., Segeroth, M., 2023. TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images. Radiology: Artificial Intelligence. https://doi.org/10.1148/ryai.230024