Nv Segment Ctmr

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

| 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.
• 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 $NVSEGMENTCTMRROOT or the repo-local fallback at .workbenchdata/upstreams/NV-Segment-CTMR/NV-Segment-CTMR.
• The default wrapper path runs automatic "segment everything" inference for CTBODY, MRIBODY, or MRIBRAIN. MRIBRAIN 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-f9f5f51/NV-Segment-CTMR}" && \
python -m pip install "monai>=1.5,=1.5,=1.5,=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.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: NVIDIA-Medtech
• Source: NVIDIA-Medtech/medical-AI-skills
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.