Nv Segment Ctmr
NVIDIAOfficialCreated by NVIDIA MedTech Team
- Skill
- Tool integration
- Science and research
- Apache-2.0
- Medium risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The nv-segment-ctmr skill provides a standardized interface for executing the NV-Segment-CTMR segmentation bundle on medical imaging volumes. It acts as a thin wrapper around the upstream MONAI-based model, facilitating inference for CT_BODY, MRI_BODY, and MRI_BRAIN modalities. The skill handles the execution of the segmentation process and records detailed evidence, including label-map metadata, voxel counts, and physical volume calculations, while explicitly excluding clinical or diagnostic use cases.
What it does
- Executes 3D segmentation on NIfTI CT or MRI volumes using the NV-Segment-CTMR foundation model.
- Supports automatic segmentation for CT_BODY, MRI_BODY, and MRI_BRAIN modalities.
- Allows for targeted segmentation by passing specific upstream class IDs via label prompts.
- Records comprehensive evidence including runtime, model inventory, and per-class physical volumes.
- Integrates with external verifiers to check anatomy plausibility and segmentation quality.
- Provides a consistent command-line interface for running inference in research environments.
When to use it
- When you need to perform automated segmentation on CT or MRI NIfTI volumes for research purposes.
- When you require a structured output of segmentation evidence, such as voxel counts and label-map metadata.
- When you need to integrate NV-Segment-CTMR inference into a larger research or data-processing pipeline.
- When you are working with preprocessed MRI brain data that requires segmentation.
Advantages
- Standardizes the execution of the complex NV-Segment-CTMR bundle.
- Provides detailed, machine-readable evidence of the segmentation process.
- Maintains compatibility with standard MONAI bundle workflows.
- Enables easy integration with automated quality verification tools.
Usage examples
- “Run CT body segmentation on my scan located at /data/patient_01.nii.gz and save the results to the runs directory.”
- The agent executes the run_ctmr.py script with the specified input path and modality, generating a label map and a result JSON file in the designated output directory.
- “Perform segmentation for specific label IDs 3 and 14 on the MRI scan at /data/mri_scan.nii.gz.”
- The agent runs the segmentation tool with the --label-prompts "3,14" argument, restricting the output to the requested anatomical classes.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/nv-segment-ctmr/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip nv-segment-ctmr.zip -d .agents/skills
Documentation by NVIDIA MedTech Team
NV-Segment-CTMR
Purpose
Used for running NV-Segment-CTMR on CT or MRI NIfTI volumes and recording label-map evidence. Not for clinical interpretation.
Use the wrapper exactly as documented; do not replace the upstream entrypoint with a handwritten implementation.
Manifest I/O: inputs are
ct_or_mr_volume; outputs arelabel_mapandresult_json.
Instructions
Read
skill_manifest.yamlbefore changing arguments, side effects, or validation gates.Run
scripts/run_ctmr.pythrough the documented command below; keep outputs under a caller-provided run directory.If a host agent exposes
run_script, userun_script("scripts/run_ctmr.py", args=[...]); otherwise run the Bash/Python command shown below.Check the emitted JSON and paired verifier guidance before treating the run as evidence.
Available Scripts
| Script | Purpose | Arguments |
|---|---|---|
scripts/run_ctmr.py | Primary entrypoint declared by skill_manifest.yaml. | PATH_TO_IMAGE.nii.gz --output-dir OUT_DIR --modality CT_BODY [--label-prompts IDS] |
Prerequisites
Runtime requirements: GPU/CUDA when declared by the manifest; Python packages listed in
runtime.side_effects.pip_packages.Optional environment variables:
NV_SEGMENT_CTMR_ROOTselects the trusted upstream checkout;CUDA_VISIBLE_DEVICESrestricts visible GPUs;MONAI_DATA_DIRECTORYandPYTORCH_CUDA_ALLOC_CONFoverride the wrapper's output-local cache and allocator defaults when needed.Side effects: writes segmentation outputs under the caller's
--output-dir, may cache model assets under~/.cache/huggingface/, and may contacthttps://github.comorhttps://huggingface.coduring setup.Run commands from the repository root unless an existing section below says otherwise.
Limitations
This is a thin wrapper. Inference, preprocessing, and postprocessing are delegated entirely to the upstream MONAI bundle under $NV_SEGMENT_CTMR_ROOT or the repo-local fallback at .workbench_data/upstreams/NV-Segment-CTMR/NV-Segment-CTMR.
The default wrapper path runs automatic "segment everything" inference for CT_BODY, MRI_BODY, or MRI_BRAIN. MRI_BRAIN inputs must already follow the upstream brain preprocessing requirements.
Label names are loaded from upstream configs when available. If a label dictionary is absent, the wrapper still records label IDs and marks only negative IDs as invalid.
No clinical, diagnostic, regulatory, or treatment-planning claims.
Not for clinical deployment, clinical interpretation, autonomous diagnosis, regulatory submission.
Troubleshooting
| Error | Cause | Fix |
|---|---|---|
| Missing dependency or import error | Runtime package drift from skill_manifest.yaml. | Install the packages declared in the manifest or use the documented setup command. |
| Empty or schema-invalid output | Wrong input path, unsupported modality, or upstream failure. | Re-run with a known fixture and inspect the wrapper JSON plus stderr. |
| Validation gate failure | Output violated a declared engineering invariant. | Keep the failed evidence pack and use the gate message to repair inputs or wrapper code. |
Wraps the upstream
NVIDIA-Medtech/NV-Segment-CTMR
CT/MRI segmentation bundle. The wrapper does not reimplement VISTA3D
inference. It shells out to the documented python -m monai.bundle run
entry point, then inspects the produced NIfTI label map.
Exact Runnable Surface
For CT body segmentation user runs and benchmark answers, use this fresh-environment-safe repo-root command shape exactly:
export NV_SEGMENT_CTMR_ROOT="${NV_SEGMENT_CTMR_ROOT:-$HOME/.cache/nvidia-skills/upstreams/NV-Segment-CTMR-cb921f5/NV-Segment-CTMR}" && \
python -m pip install "monai>=1.5,<1.6" "numpy<2" nibabel scipy typer PyYAML fire huggingface_hub pytorch-ignite einops && \
python skills/nv-segment-ctmr/scripts/run_ctmr.py PATH_TO_IMAGE.nii.gz --modality CT_BODY --output-dir OUT_DIRDo not invent python -m nv_segment_ctmr, infer.py, or Medical AI Skills run commands. PATH_TO_IMAGE.nii.gz must be the user's supplied input path.
For benchmark/user run answers, the bash block is invalid if it includes
mkdir -p .workbench_data/upstreams, git clone, mkdir -p "$NV_SEGMENT_CTMR_ROOT/models",
hf download, mv "$NV_SEGMENT_CTMR_ROOT/..., or any other command that
creates, downloads into, or moves files inside the shared upstream checkout.
Preconditions
One-time maintainer setup only; do not include these commands in user answers or benchmark commands. The benchmark environment already provides the repo-local upstream cache and model files.
If NV_SEGMENT_CTMR_ROOT already names a local bundle checkout, the wrapper
uses it and records its current commit in the result. Otherwise, clone the
recommended pinned default once:
if [ -z "${NV_SEGMENT_CTMR_ROOT:-}" ]; then
export NV_SEGMENT_CTMR_COMMIT=cb921f5c58837c0f42a713855d68b32af88e1cdd
export NV_SEGMENT_CTMR_CHECKOUT="$HOME/.cache/nvidia-skills/upstreams/NV-Segment-CTMR-cb921f5"
if [ ! -d "$NV_SEGMENT_CTMR_CHECKOUT/.git" ]; then
git clone https://github.com/NVIDIA-Medtech/NV-Segment-CTMR.git "$NV_SEGMENT_CTMR_CHECKOUT"
git -C "$NV_SEGMENT_CTMR_CHECKOUT" checkout --detach "$NV_SEGMENT_CTMR_COMMIT"
fi
export NV_SEGMENT_CTMR_ROOT="$NV_SEGMENT_CTMR_CHECKOUT/NV-Segment-CTMR"
fi
python -m pip install "monai>=1.5,<1.6" "numpy<2" nibabel scipy typer PyYAML fire huggingface_hub pytorch-ignite einops && \
python -c "import monai, nibabel, numpy"
mkdir -p "$NV_SEGMENT_CTMR_ROOT/models"
test -e "$NV_SEGMENT_CTMR_ROOT/models/model.pt" || \
hf download nvidia/NV-Segment-CTMR \
--revision 4fb8b4a6b2532be9f1c449a3726fe5440ab4213a \
--local-dir "$NV_SEGMENT_CTMR_ROOT/models/"
test -e "$NV_SEGMENT_CTMR_ROOT/models/model.pt" || \
mv "$NV_SEGMENT_CTMR_ROOT/models/vista3d_pretrained_model/model.pt" \
"$NV_SEGMENT_CTMR_ROOT/models/model.pt"The wrapper also searches .workbench_data/upstreams/NV-Segment-CTMR/NV-Segment-CTMR
if NV_SEGMENT_CTMR_ROOT is unset or does not have the required bundle layout.
For agent-generated user run commands, use the command in Usage. Do not copy
the one-time Preconditions block into the answer: do not create or write under
$NV_SEGMENT_CTMR_ROOT, do not run hf download, and do not move files in the
shared upstream checkout during a benchmark or user run. Do not prepend
pip install -r "$NV_SEGMENT_CTMR_ROOT/requirements.txt" in a Python 3.12
environment; the upstream requirements pin NumPy 1.24.4, which does not build
cleanly there. In a fresh Python environment, install the minimal compatible
runtime shown above (monai>=1.5,<1.6, numpy<2, nibabel, scipy, typer,
PyYAML, fire, huggingface_hub, pytorch-ignite, einops) before the
wrapper. Cached models do not imply cached Python packages.
Runtime needs an NVIDIA GPU with CUDA. The upstream bundle may import on CPU-only hosts, but this skill is declared as CUDA-required because the published workflow is a 3D CT/MRI foundation model inference path.
Usage
From Medical AI Skills repo root:
export NV_SEGMENT_CTMR_ROOT="${NV_SEGMENT_CTMR_ROOT:-$HOME/.cache/nvidia-skills/upstreams/NV-Segment-CTMR-cb921f5/NV-Segment-CTMR}" && \
python -m pip install "monai>=1.5,<1.6" "numpy<2" nibabel scipy typer PyYAML fire huggingface_hub pytorch-ignite einops && \
python skills/nv-segment-ctmr/scripts/run_ctmr.py PATH_TO_IMAGE.nii.gz \
--modality CT_BODY \
--output-dir runs/nv_segment_ctmr_demoReplace PATH_TO_IMAGE.nii.gz with the user's actual input path. Do not copy
the example fixture path into a user run. If the user provides an explicit
input path under runs/, that path must be the first positional argument to
scripts/run_ctmr.py.
Supported automatic segmentation modalities are CT_BODY, MRI_BODY, and
MRI_BRAIN. For MRI_BRAIN, the upstream README requires brain-specific
preprocessing before bundle inference; pass an already preprocessed image to
this wrapper.
Pass --label-prompts "3,14" to request specific upstream class IDs instead
of only the modality-level "segment everything" set. The evidence output
records input geometry, output mask path, observed label IDs, unexpected
labels, per-class voxel counts, per-class physical volumes from the mask
header spacing, runtime, upstream command, model inventory, and geometry
checks.
Pass --ground-truth PATH to record a reference label-map path under
input.ground_truth_path. The skill does not compute Dice; that is the
paired verifier's job.
Anatomy plausibility and optional per-class Dice/IoU against the recorded
ground truth can be checked by verifiers/ct_segmentation_quality_v1 for
CT-body outputs.
Not for clinical interpretation, production deployment, autonomous diagnosis, or regulatory submission.
Files
- ATTRIBUTION.mdAdded by AutoGineer916 B
- BENCHMARK.md7.7 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md9.1 KiB
- evals/evals.json1.2 KiB
- fixtures/README.md575 B
- scripts/run_ctmr.pyScript21.5 KiB
- skill-card.md4.1 KiB
- skill.oms.sig5.6 KiB
- skill_manifest.yaml7.0 KiB
- tests/test_run_ctmr.pyScript4.4 KiB
- validators/output_schema.json4.6 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Declares allowed tools, Needs a command-line tool, Needs Python
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
- Instructions that use the network
- Scripts that your agent may run
This skill includes scripts that your agent may run. Read them before you use it.
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- NVIDIA MedTech Team
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/nv-segment-ctmr · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by NVIDIA MedTech Team or NVIDIA.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download nv-segment-ctmr.zip