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Nv Segment Ctmr

NVIDIAOfficialCreated by NVIDIA MedTech Team

  • Skill
  • Tool integration
  • Science and research
  • Apache-2.0
  • Medium risk
  • Has scripts

From the author

Used for running NV-Segment-CTMR on CT or MRI NIfTI volumes and recording label-map evidence. Not for clinical interpretation.
— the description by NVIDIA MedTech Team

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

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/nv-segment-ctmr/download
  2. Unzip 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 are label_map and result_json.

Instructions

  • Read skill_manifest.yaml before changing arguments, side effects, or validation gates.

  • Run scripts/run_ctmr.py through the documented command below; keep outputs under a caller-provided run directory.

  • If a host agent exposes run_script, use run_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

ScriptPurposeArguments
scripts/run_ctmr.pyPrimary 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_ROOT selects the trusted upstream checkout; CUDA_VISIBLE_DEVICES restricts visible GPUs; MONAI_DATA_DIRECTORY and PYTORCH_CUDA_ALLOC_CONF override 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 contact https://github.com or https://huggingface.co during 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

ErrorCauseFix
Missing dependency or import errorRuntime package drift from skill_manifest.yaml.Install the packages declared in the manifest or use the documented setup command.
Empty or schema-invalid outputWrong input path, unsupported modality, or upstream failure.Re-run with a known fixture and inspect the wrapper JSON plus stderr.
Validation gate failureOutput 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_DIR

Do 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_demo

Replace 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

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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)
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