Nv Segment Ct
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
NV-Segment-CT provides a standardized interface for executing the VISTA3D segmentation model on CT NIfTI volumes. It manages the creation of an isolated Python environment, handles model bundle downloads, and executes inference via a provided script. The skill includes validation gates to verify output schema and label consistency, recording metadata such as voxel counts and physical volumes. It is intended for research and development workflows and is not for clinical interpretation or autonomous diagnosis.
What it does
- Executes VISTA3D segmentation on CT NIfTI files.
- Maps anatomical requests to specific VISTA3D class IDs.
- Creates an isolated Python 3.10 virtual environment for dependencies.
- Downloads and manages the required NVIDIA model bundle.
- Validates output masks against declared engineering invariants.
- Records segmentation evidence including label-map paths and per-class volume metrics.
When to use it
- When you need to perform automated segmentation of abdominal organs in CT scans.
- When integrating VISTA3D inference into a research pipeline.
- When you require structured evidence and metadata for segmentation outputs.
- When working with NIfTI format CT volumes in a controlled research environment.
Advantages
- Provides a consistent, reproducible wrapper for VISTA3D inference.
- Automates environment setup and dependency management.
- Includes built-in validation gates for output integrity.
- Supports clear mapping of anatomical structures to model class IDs.
Usage examples
- “Segment the liver, spleen, and kidneys in my CT scan.”
- The agent maps these to IDs 1, 3, 5, and 14, then executes the segmentation script with the --label-prompts '1,3,5,14' flag.
- “Run the NV-Segment-CT pipeline on patient_scan.nii.gz.”
- The agent initializes the virtual environment, downloads the model bundle if missing, and processes the file to generate a label map and result JSON.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/nv-segment-ct/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip nv-segment-ct.zip -d .agents/skills
Documentation by NVIDIA MedTech Team
NV-Segment-CT
Purpose
Used for running NV-Segment-CT VISTA3D on CT 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_volume; outputs arelabel_mapandresult_json.
Instructions
Read
skill_manifest.yamlbefore changing arguments, side effects, or validation gates.Run
scripts/run_vista3d.pythrough the documented command below; keep outputs under a caller-provided run directory.If a host agent exposes
run_script, userun_script("scripts/run_vista3d.py", args=[...]); otherwise run the Bash/Python command shown below.Create the documented Python 3.10 virtual environment and invoke its binaries directly; do not install model dependencies into the caller's active environment.
Check the emitted JSON and paired verifier guidance before treating the run as evidence.
Available Scripts
| Script | Purpose | Arguments |
|---|---|---|
scripts/run_vista3d.py | Primary entrypoint declared by skill_manifest.yaml. | PATH_TO_CT.nii.gz [--output-dir OUT_DIR] [--label-prompts IDS] |
Prerequisites
Runtime requirements: Python 3.10 with
venvsupport and GPU/CUDA when declared by the manifest. Model packages come from the pinned upstream requirements file; only wrapper-specific packages are added locally.Side effects: creates an isolated environment under
~/.cache/nvidia-skills/venvs/nv-segment-ct-f9f5f51/, writes the downloaded bundle underskills/nv-segment-ct/bundle/, may cache model assets under~/.cache/huggingface/, and may contacthttps://huggingface.coandhttps://raw.githubusercontent.comduring first setup; the optional spleen fixture fetcher downloads MSD09 fromhttps://msd-for-monai.s3-us-west-2.amazonaws.com.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 official
hugging_face_pipeline.HuggingFacePipelineHelperin bundle/. Do not modify code under bundle/.transformers==4.46.3is the wrapper compatibility overlay tested with the upstream requirements' Torch 2.0.1; newer Transformers releases can disable that older Torch backend.The pinned upstream requirements include Torch 2.0.1. Use only the pinned NVIDIA model assets; do not load untrusted checkpoints in this legacy reproduction environment.
Device auto-detected (cuda if available, else cpu);
--deviceflag overrides.Output may be schema-valid but semantically empty (e.g. label prompts that do not match the input anatomy). Sanity gates assert at least one foreground voxel per requested anatomy.
Not for clinical deployment, clinical interpretation, autonomous diagnosis, regulatory submission.
Troubleshooting
| Error | Cause | Fix |
|---|---|---|
ensurepip is not available while creating the environment | The host Python installation omitted its OS venv package. | Install the matching Python 3.10 venv support package or create the same isolated environment with virtualenv -p python3.10. |
| 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/NV-Segment-CT helper. The wrapper does not
reimplement VISTA3D inference.
Exact Runnable Surface
For CT segmentation user runs, use this repo-root wrapper path exactly:
"$NV_SEGMENT_CT_VENV/bin/python" skills/nv-segment-ct/scripts/run_vista3d.py PATH_TO_CT.nii.gz --label-prompts "1,3,5,14" --output-dir OUT_DIRDo not invent infer.py, Medical AI Skills run, python -m nv_segment_ct, or anatomy-name-only flags. For spleen, liver, right kidney, and left kidney, the required VISTA3D label IDs are exactly 1,3,5,14.
Preconditions
The skill assumes a Python 3.10 interpreter with venv support. Its documented
command creates a dedicated environment and installs the model dependencies
from NV-Segment-CT/requirements.txt at the immutable NVIDIA-Medtech commit
f9f5f51b589e5dc9c23c453cf5138398e4084056. The Hugging Face bundle itself
does not ship a requirements.txt.
Two one-time downloads (the documented command does the first one; the fixture fetch is a separate step you run when bootstrapping):
# Spleen example fixture from Decathlon MSD09 (~1.5 GB tar, ~11 MB
# fixture extracted into skills/nv-segment-ct/fixtures/spleen_03.nii.gz):
python skills/nv-segment-ct/fixtures/fetch_spleen_fixture.pyBoth downloads (the bundle below, and the fixture) are gitignored
(Medical AI Skills policy: no medical data or model weights in git). The fetch
script is idempotent and caches the tar under
.workbench_data/datasets/ so re-runs are no-ops.
Runtime needs an NVIDIA GPU with CUDA. CPU fallback is supported but slow.
Usage
From the skills repository root, run the complete bootstrap. Invoke the virtual environment's binaries directly so the caller's active environment is not modified:
export NV_SEGMENT_CT_VENV="${NV_SEGMENT_CT_VENV:-$HOME/.cache/nvidia-skills/venvs/nv-segment-ct-f9f5f51}"
export NV_SEGMENT_CT_REQUIREMENTS="${NV_SEGMENT_CT_REQUIREMENTS:-https://raw.githubusercontent.com/NVIDIA-Medtech/NV-Segment-CTMR/f9f5f51b589e5dc9c23c453cf5138398e4084056/NV-Segment-CT/requirements.txt}"
if [ ! -x "$NV_SEGMENT_CT_VENV/bin/python" ]; then
python3.10 -m venv "$NV_SEGMENT_CT_VENV"
fi
"$NV_SEGMENT_CT_VENV/bin/python" -m pip install \
-r "$NV_SEGMENT_CT_REQUIREMENTS" \
"transformers==4.46.3" \
"typer>=0.9"
"$NV_SEGMENT_CT_VENV/bin/hf" download nvidia/NV-Segment-CT \
--revision afb51518689f71e6abb367ee6301b2cd0225c66a \
--local-dir skills/nv-segment-ct/bundle/
"$NV_SEGMENT_CT_VENV/bin/python" skills/nv-segment-ct/scripts/run_vista3d.py PATH_TO_CT.nii.gz \
--label-prompts "1,3,5,14" \
--output-dir vista3d_outputsWhen the user names anatomies, translate them to VISTA3D class IDs before running. For the common abdominal CT request:
| Anatomy | VISTA3D class ID |
|---|---|
| liver | 1 |
| spleen | 3 |
| right kidney | 5 |
| left kidney | 14 |
For "segment the spleen, liver, right kidney, and left kidney", the correct
--label-prompts value is exactly "1,3,5,14". Do not substitute kidney
IDs from another label dictionary; the wrapper validates the requested label
set and will mark the run invalid if the emitted mask contains labels outside
the requested set.
The install and download steps are load-bearing. The pinned upstream file owns
the model environment, while Transformers and Typer support this thin wrapper.
hf download pulls the ~832 MB model bundle into
skills/nv-segment-ct/bundle/; subsequent calls reuse the caches.
label-prompts are VISTA3D class IDs. The evidence output records input
geometry, output mask path, observed label IDs, unexpected labels,
per-class voxel counts, per-class physical volumes computed from the output
mask header spacing, runtime, model identity, and fixed code-derived artifact
checks such as mask shape, affine match, label set, foreground count, and
class-volume bounds.
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 (per-class volume bounds, fragmentation, bilateral
symmetry, liver larger than spleen) and optional per-class Dice/IoU against
the recorded ground truth are checked by
verifiers/ct_segmentation_quality_v1.
Not for clinical interpretation, production deployment, or non-CT modalities.
Files
- ATTRIBUTION.mdAdded by AutoGineer910 B
- BENCHMARK.md3.9 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md8.2 KiB
- evals/evals.json1.2 KiB
- fixtures/fetch_spleen_fixture.pyScript5.2 KiB
- fixtures/generate_preflight_fixture.pyScript1.9 KiB
- scripts/run_vista3d.pyScript10.8 KiB
- skill-card.md4.1 KiB
- skill.oms.sig5.9 KiB
- skill_manifest.yaml8.3 KiB
- tests/test_run_vista3d.pyScript3.0 KiB
- validators/output_schema.json3.6 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 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-ct · 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-ct.zip