body-organ-analysis 0.1.5
BOA is a tool for segmentation of CT scans developed by the SHIP-AI group at the Institute for Artificial Intelligence in Medicine (https://ship-ai.ikim.nrw/). Combining the TotalSegmentator and the Body Composition Analysis, this tool is capable of analyzing medical images and identifying the different structures within the human body, including bones, muscles, organs, and blood vessels.
License: Apache-2.0
Requires Python: <3.11,>=3.9
Keywords: python, segmentation, nnunet, boa, body composition analysis

# BOA: Body and Organ Analysis

![BOA](images/BOA-banner.png)

BOA is a tool for segmentation of CT scans developed by the [SHIP.AI group at
the Institute for Artificial Intelligence in Medicine
(IKIM)](https://ship-ai.ikim.nrw/). Combining the
[TotalSegmentator](https://arxiv.org/abs/2208.05868) and the
[Body Composition Analysis](https://pubmed.ncbi.nlm.nih.gov/32945971/), this
tool is capable of analyzing medical images and identifying the different
structures within the human body, including bones, muscles, organs, and blood
vessels.The tool also includes functionalities for the following tasks:

- Skeleton
- Organs
- Bone Mineral Density
- Contrast Media Recognition
- Cardiovascular System
- Body Parts
- Body Tissue Composition
- Body Region Detection

## Example Segmentations

The BOA tool can be used to generate full body segmentations of CT scans:

![Segmentation of human body](https://raw.githubusercontent.com/UMEssen/Body-and-Organ-Analysis/main/images/segmentation.png)

Additionally, the generated segmentations can be used as input to generate
realistic images using [Siemens' Cinematic Rendering](https://www.siemens-healthineers.com/digital-health-solutions/cinematic-rendering).

![Cinematic rendering](https://raw.githubusercontent.com/UMEssen/Body-and-Organ-Analysis/main/images/cinematic.svg)

## Citation

If you use this tool, please cite the following papers:

[BOA](https://journals.lww.com/investigativeradiology/abstract/9900/boa__a_ct_based_body_and_organ_analysis_for.176.aspx):

```text
Haubold, J., Baldini, G., Parmar, V., Schaarschmidt, B. M., Koitka, S., Kroll, 
L., van Landeghem, N., Umutlu, L., Forsting, M., Nensa, F., & Hosch, R. (2023). 
BOA: A CT-Based Body and Organ Analysis for Radiologists at the Point of Care. 
Investigative radiology, 10.1097/RLI.0000000000001040. Advance online 
publication. https://doi.org/10.1097/RLI.0000000000001040
```

[TotalSegmentator](https://pubs.rsna.org/doi/10.1148/ryai.230024):

```text
Wasserthal J, Breit H-C, Meyer MT, et al. TotalSegmentator: Robust Segmentation 
of 104 Anatomic Structures in CT Images. Radiol. Artif. Intell. 2023:e230024. 
Available at: https://pubs.rsna.org/doi/10.1148/ryai.230024.
```

[nnU-Net](https://www.nature.com/articles/s41592-020-01008-z):

```text
Isensee F, Jaeger PF, Kohl SAA, et al. nnU-Net: a self-configuring method for 
deep learning-based biomedical image segmentation. Nat. Methods. 
2021;18(2):203–211. Available at: https://www.nature.com/articles/s41592-020-01008-z.
```

## How to run?

- Set up the [environment variables](./documentation/environment_variables.md).
- Either use the [PACS integration](./documentation/pacs_integration.md) or the
[command line tool](./documentation/command_line.md).

## Notes on Performance

To make an estimate on how much power and time is needed to process a study, we
used the [following table](https://github.com/wasserth/TotalSegmentator/blob/master/resources/imgs/runtime_table.png)
provided by the TotalSegmentator. However, for very large series
(e.g. 1600 slices 1mm), the performance may be worse and more CPU power may be
needed. According to our tests, 16GB of GPU should be sufficient.