mcpbeat

I4h Catheter Navigation Digital Twin

nvidia/i4h-catheter-navigation-digital-twin

Build a patient vasculature digital twin from CT (preprocess + segment). Use when asked to preprocess CT, segment vessels, extract centerline, or prepare ct_cache for viewport/DRR.

6k tokens
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the whole folder, loaded on every use
5
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instructions only
0
copies elsewhere
how many repositories repackaged it
2778
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/NVIDIA/skills --skill i4h-catheter-navigation-digital-twin

What comes with it

18 067 bytes besides the instruction
BENCHMARK.md
evals/evals.json
skill-card.md
skill.oms.sig

The instruction itself

15 sections, as written by the author

i4h Catheter Navigation - Digital Twin

Purpose

Download or locate a CT volume, preprocess it to an attenuation cache, and segment the arterial tree into vessel mask + centerline - the vasculature digital twin required for patient-specific viewport and DRR runs.

Base Code

ROOT="${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>/dev/null)}"
if [ ! -d "$ROOT/workflows/catheter_navigation" ]; then
  ROOT="${I4H_WORKFLOWS:-$HOME/i4h-workflows}"
  [ -d "$ROOT/workflows/catheter_navigation" ] || git clone https://github.com/isaac-for-healthcare/i4h-workflows "$ROOT"
fi
export I4H_WORKFLOWS="$ROOT"; cd "$ROOT"

Basics

  • Output cache layout: --output-dir / --ct-dir (e.g. /tmp/ct_cache) holds mu_volume.npy, metadata.json, and after segmentation vessel mask + centerline artifacts.
  • Contrast-enhanced CTA subjects work best; TotalSegmentator small subset (~3.2 GB) is the documented public dataset.
  • Comply with the dataset license; no patient data is committed to the repo.

Run

Run the steps below in order. Each step is a separate bash call; variables persist in the local agent's tmux session.

Step 1 - resolve paths

REPO_ROOT="${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>/dev/null)}"; [ -d "$REPO_ROOT/workflows/catheter_navigation" ] || REPO_ROOT="$HOME/i4h-workflows"
WF_ROOT="${REPO_ROOT}/workflows/catheter_navigation"
RUN_DIR="${WF_ROOT}/runs/digital_twin_$(date +%Y%m%d_%H%M%S)"
mkdir -p "${RUN_DIR}/logs"
ln -sfn "${RUN_DIR}" "${WF_ROOT}/runs/.latest"

# User-supplied or downloaded subject directory (must contain ct.nii.gz + segmentations/)
SUBJ="${SUBJ:-}"
CACHE="${CACHE:-/tmp/ct_cache}"

if [ -z "${SUBJ}" ] || [ ! -f "${SUBJ}/ct.nii.gz" ]; then
  echo "digital-twin: set SUBJ to an extracted TotalSegmentator subject (got '${SUBJ:-<unset>}')." >&2
  echo "Example: SUBJ=/path/to/Totalsegmentator_dataset_small_v201/s0011" >&2
  exit 1
fi

Step 2 - download dataset (skip if SUBJ already exists)

Only run when the user has no CT data yet.

curl -L "https://www.dropbox.com/scl/fi/pee5yxebfxrhz007cbuy5/Totalsegmentator_dataset_small_v201.zip?rlkey=osvfk02jc4lw5gr6uhrldtb9e&dl=1" \
  -o "${RUN_DIR}/Totalsegmentator_dataset_small_v201.zip"
unzip "${RUN_DIR}/Totalsegmentator_dataset_small_v201.zip" -d "${RUN_DIR}/Totalsegmentator_dataset_small_v201"
ls "${RUN_DIR}/Totalsegmentator_dataset_small_v201"
# Then set SUBJ to one extracted subject before continuing.

Step 3 - preprocess CT

"${REPO_ROOT}/i4h" run catheter_navigation preprocess_ct --local \
  --run-args="--nifti ${SUBJ}/ct.nii.gz --output-dir ${CACHE} --save-hu" \
  2>&1 | tee "${RUN_DIR}/logs/preprocess_ct.log"

Step 4 - segment vessels

"${REPO_ROOT}/i4h" run catheter_navigation segment_vessels --local \
  --run-args="--ct-dir ${CACHE} --ts-gt-dir ${SUBJ}/segmentations" \
  2>&1 | tee "${RUN_DIR}/logs/segment_vessels.log"

Verify

test -f "${CACHE}/mu_volume.npy"
test -f "${CACHE}/metadata.json"
ls -la "${CACHE}"

Notes

  • SUBJ must point at one extracted subject with ct.nii.gz and segmentations/ (TotalSegmentator layout).
  • CACHE is reused by [[i4h-catheter-navigation-viewport]] and cache-based [[i4h-catheter-navigation-render-drr]].
  • Segmentation is CPU/GPU mixed and may take several minutes depending on volume size.

Prerequisites

  • [[i4h-catheter-navigation-setup]] completed (imports and CLI work).
  • A CT NIfTI and matching vessel segmentations (or TotalSegmentator subject).
  • >= 32 GB RAM recommended for large volumes.

Limitations

  • Does not ship data; user must download or provide their own CT.
  • Zenodo mirror is throttled; prefer the Dropbox URL in Step 2.

Troubleshooting

  • Error: SUBJ unset or missing ct.nii.gz - Fix: download Step 2 dataset or set SUBJ to an existing subject path.
  • Error: segment_vessels fails on --ts-gt-dir - Fix: confirm ${SUBJ}/segmentations exists (TotalSegmentator ground truth).
  • Error: out of memory during preprocess - Fix: use a smaller subject or increase swap; close other GPU/CPU workloads.

Final Response

Report CACHE path, key artifacts present, log paths under RUN_DIR, and recommend [[i4h-catheter-navigation-viewport]] or [[i4h-catheter-navigation-render-drr]] next.

How to use it

Copy the folder

Take nvidia/i4h-catheter-navigation-digital-twin from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.