Nv Generate Ct Rflow
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-generate-ct-rflow skill provides a standardized interface for the NVIDIA-Medtech NV-Generate-CTMR rectified-flow synthesis pipeline. It manages configuration staging, output path handling, and validation for generating synthetic CT volumes and paired masks. The skill includes helper utilities for anatomy lookup, diagnostic mask generation, and image-only synthesis, while delegating core inference and sampling to the upstream repository.
What it does
- Generates synthetic CT volumes and corresponding label masks.
- Supports controllable anatomy generation for specific tumor types.
- Provides diagnostic tools for raw mask generation and image-from-mask synthesis.
- Validates input configurations against schema bounds and anatomy constraints.
- Produces summary cards for visual inspection of generated outputs.
- Maps paired output labels back to standard MAISI label IDs.
When to use it
- When you need to generate synthetic CT image data for research purposes.
- When you require paired CT volumes and segmentation masks.
- When you need to perform controllable synthesis of specific anatomical regions or tumors.
- When you need to validate configuration files before executing large-scale inference.
Advantages
- Automates environment setup and dependency management for the upstream pipeline.
- Ensures consistent output structure and metadata recording.
- Provides built-in preflight checks to prevent common configuration errors.
- Simplifies complex inference workflows through standardized wrapper commands.
Usage examples
- “Generate a synthetic CT volume using the chest lung tumor controllable configuration.”
- The agent executes the wrapper script with the specified configuration, producing synthetic CT volumes and masks in the designated output directory.
- “List all available chest anatomies for generation.”
- The agent runs the anatomy lookup tool and returns a list of 132-class labels grouped by the chest region.
- “Validate the abdomen liver and spleen configuration without running full inference.”
- The agent performs a preflight check on the configuration file and reports any schema or constraint violations without generating image data.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/nv-generate-ct-rflow/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip nv-generate-ct-rflow.zip -d .agents/skills
Documentation by NVIDIA MedTech Team
NV-Generate-CT (rflow-ct)
Purpose
Used for generating synthetic CT volumes and masks with NV-Generate-CTMR rflow-ct. Not for production training data without review.
Use the wrapper exactly as documented; do not replace the upstream entrypoint with a handwritten implementation.
Do not write custom inference code for normal runs. The wrapper owns config staging, output paths, label mapping evidence, and validation.
Manifest I/O: inputs are
config_infer_override; outputs aresynthetic_ct_volumesandresult_json.
Instructions
Read
skill_manifest.yamlbefore changing arguments, side effects, or validation gates.Run
scripts/run_rflow_ct.pythrough the documented command below; keep outputs under a caller-provided run directory.If a host agent exposes
run_script, userun_script("scripts/run_rflow_ct.py", args=[...]); otherwise run the Bash/Python command shown below.Emit a single bash code block, and keep the
python -m pip install -r "$NV_GENERATE_ROOT/requirements.txt"step in that same command — the runtime may be a fresh environment withoutnibabel/MONAI, so dropping the install fails withModuleNotFoundError.Do not add
rm,mkdir, or any cleanup of--output-dir; the wrapper creates it. Use a fresh--output-dirinstead of deleting one.Check the emitted JSON and paired verifier guidance before treating the run as evidence.
Available Scripts
| Script | Purpose | Arguments |
|---|---|---|
scripts/_anatomy.py | Internal helper used by the primary entrypoint. | Imported only; do not call directly. |
scripts/_summary_card.py | Internal helper used by the primary entrypoint. | Imported only; do not call directly. |
scripts/list_anatomies.py | Helper command for catalog or anatomy lookup. | [--region REGION] [--filter TEXT] [--controllable] |
scripts/run_rflow_ct.py | Primary entrypoint declared by skill_manifest.yaml. | CONFIG_INFER.json --output-dir OUT_DIR [--random-seed N] [--version rflow-ct] [--yes] |
scripts/run_ct_mask.py | Advanced diagnostic helper for standalone raw MAISI mask generation. | REQUEST.json --output-dir OUT_DIR [--random-seed N] [--preflight-only] [--yes] |
scripts/run_ct_from_mask.py | Advanced helper for CT image generation from a MAISI label mask. | REQUEST.json --output-dir OUT_DIR [--random-seed N] [--yes] |
scripts/run_ct_image.py | Advanced helper for CT image-only generation without paired labels. | MODEL_CONFIG.json --output-dir OUT_DIR [--version rflow-ct] [--random-seed N] [--yes] |
Prerequisites
Required environment variables:
NV_GENERATE_ROOT.Runtime requirements: GPU/CUDA when declared by the manifest; Python packages listed in
runtime.side_effects.pip_packages.Side effects: writes generated outputs under the caller's
--output-dir, may cache model assets under~/.cache/huggingface/, and may contacthttps://huggingface.coorhttps://github.comduring setup.Run commands from the repository root unless an existing section below says otherwise.
Limitations
This is a thin wrapper. Inference, sampling, and decoding are delegated entirely to NVIDIA-Medtech/NV-Generate-CTMR's
scripts.inference. Do not modify code under $NV_GENERATE_ROOT.rflow-ct requires CUDA and ≈ 16 GB VRAM minimum for the default 256³ output_size. Larger output_size (e.g. 512×512×768) needs an A100/H100.
Output volumes are synthetic. They are not safe to use as training data for production medtech models without an independent quality review.
Not for clinical deployment, clinical interpretation, autonomous diagnosis, regulatory submission.
Troubleshooting
| Error | Cause | Fix |
|---|---|---|
| Missing dependency or import error | Runtime package drift from skill_manifest.yaml. | Install the packages declared in the manifest or use the documented setup command. |
| Empty or schema-invalid output | Wrong input path, unsupported modality, or upstream failure. | Re-run with a known fixture and inspect the wrapper JSON plus stderr. |
| Validation gate failure | Output violated a declared engineering invariant. | Keep the failed evidence pack and use the gate message to repair inputs or wrapper code. |
Wraps the upstream
NVIDIA-Medtech/NV-Generate-CTMR
rectified-flow synthesis pipeline. The wrapper does not reimplement diffusion,
sampling, or autoencoder decoding — it shells out to the upstream
scripts.inference entry point exactly as the project's README documents and
inspects the produced image/mask pairs.
Preconditions
If
NV_GENERATE_ROOTalready names a local checkout, the wrapper uses it and records its current commit in the result. Otherwise, create the recommended pinned default checkout (one-time):if [ -z "${NV_GENERATE_ROOT:-}" ]; then export NV_GENERATE_COMMIT=61c4ec709b84cad468852243c48e250bec732074 export NV_GENERATE_ROOT="$HOME/.cache/nvidia-skills/upstreams/NV-Generate-CTMR-61c4ec7" if [ ! -d "$NV_GENERATE_ROOT/.git" ]; then git clone https://github.com/NVIDIA-Medtech/NV-Generate-CTMR.git "$NV_GENERATE_ROOT" git -C "$NV_GENERATE_ROOT" checkout --detach "$NV_GENERATE_COMMIT" fi fi python -m pip install -r "$NV_GENERATE_ROOT/requirements.txt"Download the
rflow-ctweights and the mask-candidate datasets into the clone (one-time, ≈ 5.5 GB):cd "$NV_GENERATE_ROOT" python -m scripts.download_model_data --version rflow-ct --root_dir "./"The mask candidates (
datasets/all_masks_flexible_size_and_spacing_4000) condition the diffusion sampler; omitting them via--model_onlywill make the inference script fail with a missing-file error at startup. The anatomy-size condition file is also part of the full CT download and is needed for controllable mask generation.NVIDIA GPU with ≥ 16 GB VRAM and CUDA. There is no CPU fallback.
For agent-generated user run commands, prefer the short wrapper command in
Usage. Do not prepend clone or model-download setup steps when NV_GENERATE_ROOT
or the repo-local upstream cache is already present. In a fresh Python
environment, still include python -m pip install -r "$NV_GENERATE_ROOT/requirements.txt"
before the wrapper unless the active environment has already proven those
imports are available; cached weights do not imply cached Python packages. Run
the wrapper from the medical-AI-skills repo root. If setup requires cd "$NV_GENERATE_ROOT", return to the Medical AI Skills repo before invoking
skills/nv-generate-ct-rflow/scripts/run_rflow_ct.py.
Usage
export NV_GENERATE_ROOT="${NV_GENERATE_ROOT:-$HOME/.cache/nvidia-skills/upstreams/NV-Generate-CTMR-61c4ec7}" && \
python -m pip install -r "$NV_GENERATE_ROOT/requirements.txt" && \
python skills/nv-generate-ct-rflow/scripts/run_rflow_ct.py \
PATH_TO_CONFIG_INFER.json \
--output-dir runs/nv_generate_ct_rflow_demo \
--random-seed 0 \
--version rflow-ctReplace PATH_TO_CONFIG_INFER.json with the user's actual request/config
path. Do not copy the fixture path from this document unless the user
explicitly asked to run that fixture. If the user says "the case request is at
runs/.../chest_lung_tumor_controllable.json", that exact path is the first
positional argument to scripts/run_rflow_ct.py.
The fixture argument is a config_infer.json override file: it can replace
num_output_samples, body_region, anatomy_list, controllable_anatomy_size,
output_size, and spacing. Pass default to use the upstream config
verbatim. The wrapper stages the override into the upstream tree before
running.
Fixture catalog
fixtures/ ships curated configs for common paired synthesis use cases: chest
lung lobes, chest with controllable lung tumor, abdomen solid organs,
abdomen with controllable hepatic tumor, head + cervical spine, pelvis.
See fixtures/README.md for the full table.
Helper commands
# Browse the 132-class label_dict grouped by body region.
python skills/nv-generate-ct-rflow/scripts/list_anatomies.py --region chest
python skills/nv-generate-ct-rflow/scripts/list_anatomies.py --controllable
python skills/nv-generate-ct-rflow/scripts/list_anatomies.py --filter tumor
# Validate a fixture and preview cost without launching inference.
NV_GENERATE_ROOT=$HOME/.cache/nvidia-skills/upstreams/NV-Generate-CTMR-61c4ec7 \
python skills/nv-generate-ct-rflow/scripts/run_rflow_ct.py \
skills/nv-generate-ct-rflow/fixtures/abdomen_liver_spleen.json \
--output-dir runs/preview --preflight-onlyAdvanced helpers stay inside this skill for debugging and less-common CT generation modes. Use them only when the user explicitly asks for that mode:
# Raw MAISI mask diagnostic, useful for checking lung tumor -> label 23.
python skills/nv-generate-ct-rflow/scripts/run_ct_mask.py \
skills/nv-generate-ct-rflow/fixtures/ct_mask_lung_tumor.json \
--output-dir runs/ct_mask_debug --preflight-only
# CT image from an existing MAISI label mask with body label 200.
python skills/nv-generate-ct-rflow/scripts/run_ct_from_mask.py \
skills/nv-generate-ct-rflow/fixtures/ct_from_mask_request_example.json \
--output-dir runs/ct_from_mask_demo
# CT image-only generation without paired labels.
python skills/nv-generate-ct-rflow/scripts/run_ct_image.py \
skills/nv-generate-ct-rflow/fixtures/ct_image_only_default.json \
--output-dir runs/ct_image_only_demo --version rflow-ctThe wrapper runs preflight on every invocation (regardless of
--preflight-only): config-schema bounds, anatomy names matched
against the upstream label_dict, body_region in the supported set,
controllable_anatomy_size constraints, upstream CT output-size/spacing
contracts, body-region-aware x/y FOV minimums, dataset presence under
$NV_GENERATE_ROOT/datasets/, CUDA available, and an estimated peak VRAM /
wall-time. Runs estimated to exceed 5 min wall-time or 30 GB VRAM peak require
--yes to proceed.
Each invocation runs python -m scripts.inference -t configs/config_network_rflow.json -i configs/config_infer.json -e configs/environment_rflow-ct.json --random-seed <s> --version rflow-ct. Output evidence records the upstream git commit, model
checkpoint hashes, the rendered config, per-sample image/mask geometry, mask
label set, image HU range summary, and per-class voxel volumes.
When controllable_anatomy_size is non-empty, upstream ignores the broader
anatomy_list for the saved paired label map and filters labels to the
controllable anatomy names. The saved paired label values are local 1..N
ordinals, not raw MAISI label IDs. Read output.output_label_mapping in
result_json to map saved output labels back to source labels; for example,
output label 1 can represent MAISI label 23 (lung tumor).
For curated lung-tumor examples, prefer a controllable size around 0.5 or
larger; smaller requests such as 0.2 can produce absent or extremely small
label-23 components for some seeds.
For FOV and setup details, see references/fov-and-downloads.md. For
advanced helper label-space details, see
references/ct-mask-label-space.md and references/ct-from-mask-format.md.
Visual sample card
Alongside the NIfTI pairs, the wrapper writes summary.html to the
output directory: a per-sample mid-slice triptych (axial / coronal /
sagittal) with label overlay, plus a table of the rendered config and
verifier-facing aggregates. Lets you eyeball the result without firing
up 3D Slicer. Pass --no-summary-card to skip.
Anatomy plausibility (label-set sanity, voxel HU range as CT, image/mask
geometry match, declared output labels present, lung-lobe HU floor) is checked by
verifiers/ct_synthesis_quality_v1.
Not for clinical interpretation, training data for production deployment, or any non-synthetic-research use.
Files
- ATTRIBUTION.mdAdded by AutoGineer931 B
- BENCHMARK.md3.9 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md12.0 KiB
- evals/evals.json1.4 KiB
- fixtures/README.md4.4 KiB
- fixtures/abdomen_hepatic_tumor.json787 B
- fixtures/abdomen_liver_spleen.json747 B
- fixtures/chest_lung_lobes.json680 B
- fixtures/chest_lung_tumor_controllable.json850 B
- fixtures/ct_from_mask_request_example.json204 B
- fixtures/ct_image_only_default.json284 B
- fixtures/ct_mask_lung_tumor.json413 B
- fixtures/default_config_infer.json679 B
- fixtures/head_brain.json710 B
- fixtures/pelvis.json707 B
- references/ct-from-mask-format.md672 B
- references/ct-mask-label-space.md761 B
- references/fov-and-downloads.md2.0 KiB
- scripts/_anatomy.pyScript11.8 KiB
- scripts/_summary_card.pyScript10.9 KiB
- scripts/list_anatomies.pyScript4.5 KiB
- scripts/run_ct_from_mask.pyScript17.5 KiB
- scripts/run_ct_image.pyScript20.4 KiB
- scripts/run_ct_mask.pyScript20.0 KiB
- scripts/run_rflow_ct.pyScript38.6 KiB
- scripts/wrapper_utils.pyScript2.1 KiB
- skill-card.md4.2 KiB
- skill.oms.sig11.1 KiB
- skill_manifest.yaml7.9 KiB
- tests/test_run_ct_from_mask.pyScript2.7 KiB
- tests/test_run_ct_image.pyScript3.2 KiB
- tests/test_run_ct_mask.pyScript2.9 KiB
- tests/test_run_rflow_ct.pyScript12.0 KiB
- validators/output_schema.json6.4 KiB
LICENSE
Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright [yyyy] [name of copyright owner]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
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-generate-ct-rflow · 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-generate-ct-rflow.zip