nv-segment-ctmr

Original：🇺🇸 English
Translated
2 scripts

Used for running NV-Segment-CTMR on CT or MRI NIfTI volumes and recording label-map evidence. Not for clinical interpretation.

9installs
Sourcenvidia/skills
Added on

NPX Install

npx skill4agent add nvidia/skills nv-segment-ctmr

Tags

Translated version includes tags in frontmatter
nv-segment-ctmrmedical-image-segmentationmonai-bundlenifti-processingcuda-inference

SKILL.md Content

View Translation Comparison →

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.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
    .
  • 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:
bash
export NV_SEGMENT_CTMR_ROOT="${NV_SEGMENT_CTMR_ROOT:-.workbench_data/upstreams/NV-Segment-CTMR/NV-Segment-CTMR}" && \
python -m pip install "monai>=1.5,<1.6" "numpy<2" nibabel scipy typer PyYAML fire huggingface_hub pytorch-ignite einops && \
python skills/nv-segment-ctmr/scripts/run_ctmr.py PATH_TO_IMAGE.nii.gz --modality CT_BODY --output-dir OUT_DIR
Do not invent 
python -m nv_segment_ctmr
, 
infer.py
, or 
Medical AI Skills run
commands. 
PATH_TO_IMAGE.nii.gz
must be the user's supplied input path. For benchmark/user run answers, the bash block is invalid if it includes 
mkdir -p .workbench_data/upstreams
, 
git clone
, 
mkdir -p "$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.
Clone and install the upstream bundle once. In this Medical AI Skills checkout, prefer the repo-local cache path when it exists:
bash
mkdir -p .workbench_data/upstreams
test -d .workbench_data/upstreams/NV-Segment-CTMR/.git || \
  git clone https://github.com/NVIDIA-Medtech/NV-Segment-CTMR.git \
    .workbench_data/upstreams/NV-Segment-CTMR
export NV_SEGMENT_CTMR_ROOT=.workbench_data/upstreams/NV-Segment-CTMR/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 -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 --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 points at a stale clone.
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:
bash
export NV_SEGMENT_CTMR_ROOT="${NV_SEGMENT_CTMR_ROOT:-.workbench_data/upstreams/NV-Segment-CTMR/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.