body-organ-analysis 1.0.1

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.

BOA: Body and Organ Analysis

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BOA is a tool for segmentation of CT scans developed by the SHIP.AI group at the Institute for Artificial Intelligence in Medicine (IKIM). Combining the TotalSegmentator and the Body Composition Analysis, this tool analyzes medical images and identifies the different structures within the human body, including bones, muscles, organs, and blood vessels. It 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

BOA can be used in two ways:

• as a command line tool that runs every segmentation in one go on a single CT (no PACS required), or
• as a PACS integration that receives DICOM studies, segments them automatically, and pushes the results back to your infrastructure.

Platform support. BOA inference runs on Linux and Windows (Docker Desktop / WSL2), either with an NVIDIA GPU or CPU-only (no GPU required, just slower). macOS is not supported for inference at this time.

Example segmentations

BOA can generate full-body segmentations of CT scans:

Generated from study s0763 of the TotalSegmentator dataset (J. Wasserthal / University Hospital Basel), licensed under CC BY 4.0.

The generated segmentations can additionally be used as input to produce realistic renderings with Siemens' Cinematic Rendering:

Requirements

• Docker with Compose v2.
• An NVIDIA GPU with the NVIDIA Container Toolkit for GPU inference (≥ 16 GB GPU memory is sufficient for our tests). A GPU is recommended but not required — BOA also runs CPU-only, just considerably slower. (Triton-based CPU acceleration is planned but not available yet.)
• Some of the advanced models require a TotalSegmentator license (see Models).

The prebuilt images are published on Docker Hub under the shipai/ namespace: shipai/boa-cli, shipai/boa-orthanc, shipai/boa-rabbitmq, shipai/boa-worker-gpu, and shipai/boa-worker-cpu.

Command line tool

Use the CLI to segment a single study without connecting to a PACS.

Pull the image:

docker pull shipai/boa-cli

or clone the repository and build it yourself:

source scripts/generate_version.sh
docker build -t shipai/boa-cli --file scripts/cli.dockerfile .

Faster rebuilds (optional). Pass --build-arg BUILD_CACHE=1 to keep a persistent uv download/build cache that survives even --no-cache rebuilds, so dependencies are not re-downloaded every time:

DOCKER_BUILDKIT=1 docker build --build-arg BUILD_CACHE=1 \
    -t shipai/boa-cli --file scripts/cli.dockerfile .

It is off by default (the build behaves exactly as without it) and requires BuildKit. The cache lives on the build host and never ends up in the image, so image size is unaffected.

Then run it. The image bind-mounts an input (NIfTI file or a DICOM directory), an output directory, and a host directory for the model weights so they are cached between runs. Ownership of the outputs is controlled by DOCKER_USER (uid:gid); do not use --user, as that bypasses the container entrypoint that fixes file permissions.

docker run --rm \
    -v "$INPUT":/dicoms \
    -v "$OUTPUT_DIR":/workspace \
    -v "$LOCAL_WEIGHTS_PATH":/app/weights \
    --gpus all \
    --shm-size=8g --ulimit memlock=-1 --ulimit stack=67108864 \
    -e DOCKER_USER="$(id -u):$(id -g)" \
    shipai/boa-cli \
    python -m body_organ_analysis \
        --input-image /dicoms \
        --output-dir /workspace \
        --models total+bca \
        --verbose

• $INPUT is either a DICOM directory or a single .nii.gz file (mount it to /dicoms or, for a NIfTI, e.g. /dicoms/image.nii.gz and point --input-image at it).
• $OUTPUT_DIR is where the results are written.
• On Linux you may use --runtime=nvidia instead of --gpus all; to pin a specific GPU use --gpus '"device=0"'.
• For CPU-only runs drop --gpus all and pass -e DEVICE=cpu. The --fast-bca/--fast-total modes are strongly recommended on CPU to keep runtimes reasonable; even so, expect considerably longer runtimes than on a GPU.

Useful CLI options

Run body_organ_analysis --help for the full list. The most common:

Option	Purpose
-m, --models	Plus-separated models to run, e.g. total+bca, or all. Required. See Models.
-d, --device	gpu, cuda, gpu:<id>, cuda:<id>, or cpu.
-l, --license_number	TotalSegmentator license number (needed for some models).
--fast-total	Run TotalSegmentator in fast mode.
--fast-bca	Use the single-fold BCA variant instead of the 5-fold ensemble.
--bca-no-pdf	Skip the PDF report; still write the bca-measurements.json.
--bca-examined-body-region	Limit the BCA report to abdomen, neck, or thorax.
--cnr-adjustment	Add a CNR-adjusted Excel sheet for supported regions (aorta + autochthon from total). The pulmonary artery measurement additionally requires heartchambers_highres; without it, that one column is skipped and a warning is logged.
--skip-contrast-information	Skip IV-phase / GIT-contrast prediction.
--theme	light (default) or dark for the BCA PDF.
-p, --preview	Generate a PNG preview of the TotalSegmentator output.
-r, --radiomics	Also compute radiomics features (experimental).
--use-study-prefix	Prefix every output file with the input file name.
-v, --verbose	Print BOA progress logs.

Most flags also have an environment-variable equivalent that is useful for the PACS integration: DEVICE, THEME, LICENSE_NUMBER, FAST_BCA, FAST_TOTAL, BCA_NO_PDF, SKIP_CONTRAST_INFORMATION, and VERBOSE (booleans accept 1/true).

PACS integration

For automated, hands-off processing, deploy the Compose stack: an Orthanc DICOM receiver queues each incoming series through RabbitMQ to a Celery worker, which runs the segmentation and pushes the results to a local folder, an SMB share, and/or a DicomWeb endpoint. An optional PostgreSQL database collects per-task statistics for monitoring.

DICOM sender ──► Orthanc ──► RabbitMQ ──► worker (GPU or CPU) ──► outputs
                                                                 ├── local folder
                                                                 ├── SMB share (Excel + report)
                                                                 └── DicomWeb (DICOM-SEG)

A single, cross-platform docker-compose.yml drives the whole stack on both Linux and Windows. See the PACS integration guide for the full deployment walkthrough, the environment-variable reference, monitoring setup, and the list of produced outputs.

Quick start:

# 1. Configure the stack (copy and edit the sample).
cp .env_sample .env

# 2. Build the images and start the services.
docker compose build orthanc rabbitmq worker-gpu
docker compose up -d orthanc rabbitmq worker-gpu monitoring

Faster rebuilds (optional). The orthanc, worker-gpu, and worker-cpu images accept a BUILD_CACHE build arg (default 0, off) that keeps a persistent pip/uv cache surviving even --no-cache rebuilds. Enable it by setting BUILD_CACHE=1 for the build — e.g. in .env or inline:

BUILD_CACHE=1 docker compose build orthanc rabbitmq worker-gpu

It requires BuildKit (Compose v2 uses it by default) and the cache stays on the build host, so the resulting images are unchanged.

Use worker-cpu instead of worker-gpu if you do not have a local GPU; it runs the same segmentations on the CPU (slower). On CPU, enabling the fast modes (FAST_TOTAL=true / FAST_BCA=true) is strongly recommended to keep runtimes reasonable.

Models

Select models with --models (CLI) or the PACS_MODEL environment variable (PACS stack), as a +-separated list or all. Unknown names are rejected on the CLI and dropped with a warning in the PACS stack. Requesting bca automatically adds total, because BCA depends on it. all runs every model in the table below; heartchambers_highres is licensed and is included in all only when a valid license (-l/--license_number or LICENSE_NUMBER) is supplied — otherwise request it explicitly by name (e.g. total+heartchambers_highres).

Model	Output	Notes
total	total.nii.gz	Full-body TotalSegmentator (100+ structures).
body_parts	body_parts.nii.gz	Body and extremities.
body_regions	body_regions.nii.gz	Body regions (abdomen / thorax / brain). Also produced internally by bca.
bca	tissues.nii.gz, body_regions.nii.gz, report.pdf	Body Composition Analysis. Implicitly adds total; always runs body_parts + body_regions internally.
lung_vessels	lung_vessels_airways.nii.gz	Lung vessels and airways.
cerebral_bleed	cerebral_bleed.nii.gz	Intracerebral hemorrhage.
hip_implant	hip_implant.nii.gz	Hip implant.
liver_segments	liver_segments.nii.gz	Couinaud liver segments.
liver_vessels	liver_vessels.nii.gz	Liver vessels and tumor.
pleural_pericard_effusion	pleural_pericard_effusion.nii.gz	Pleural / pericardial effusion.
heartchambers_highres	heartchambers_highres.nii.gz	High-resolution heart chambers. Added to all only with a valid license; otherwise request explicitly. Needed for the pulmonary artery column of the --cnr-adjustment sheet.

Several of the specialized models (e.g. heartchambers_highres, liver_vessels, lung_vessels, pleural_pericard_effusion) require a TotalSegmentator license, passed with -l/--license_number or the LICENSE_NUMBER environment variable.

Outputs

Every run produces an output.xlsx workbook (renamed to AccessionNumber_SeriesNumber_SeriesDescription.xlsx in the PACS stack) with a subset of the following sheets:

Sheet	When
info	Always — patient/study metadata, BOA version, contrast prediction.
regions-statistics	When total (or another measured model) is run — per-region volume and statistics.
cnr-adjusted	With --cnr-adjustment — image-quality-adjusted measurements.
bca-aggregated-measurements	With bca — aggregated tissue measurements (also shown in the report).
bca-slice-measurements	With bca — per-slice tissue volumes.
bca-slice-measurements_no_ext	With bca — the same, with extremities excluded.

Alongside the workbook you get the segmentation NIfTIs, a report.pdf (BCA), preview_total.png/.pdf, and *-measurements.json files. In the PACS stack, the Excel/report can be uploaded to an SMB share and the segmentations to a DicomWeb endpoint as DICOM-SEG objects.

Performance

To estimate the compute and time required to process a study, see the runtime table provided by TotalSegmentator. For very large series (e.g. 1600 slices at 1 mm) performance can be worse and more CPU power may be needed. According to our tests, 16 GB of GPU memory is sufficient.

Citation

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

BOA:

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:

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:

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.

License

BOA is released under the Apache License 2.0. See LICENSE. The underlying TotalSegmentator models carry their own license terms.