I4h catheter navigation digital twin
Skill NVIDIA/skills/skills/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.From its SKILL.md
npx -y skills add NVIDIA/skills --skill i4h-catheter-navigation-digital-twinAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
4.7 KB, ~1.2k tokens by cl100k_base, as published. Nobody here has run it
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 "$ROOT"
Basics
- Output cache layout:
--output-dir/--ct-dir(e.g./tmp/ct_cache) holdsmu_volume.npy,metadata.json, and after segmentation vessel mask + centerline artifacts. - Contrast-enhanced CTA subjects work best; TotalSegmentator small subset (~3.2 GB) is the documented public dataset.
- Comply with the dataset license; no patient data is committed to the repo.
Run
Run the steps below in order. Each step is a separate bash call; variables persist in the local agent's tmux session.
Step 1 - resolve paths
REPO_ROOT="${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 "${CACHE}"
Notes
SUBJmust point at one extracted subject withct.nii.gzandsegmentations/(TotalSegmentator layout).CACHEis reused by [[i4h-catheter-navigation-viewport]] and cache-based [[i4h-catheter-navigation-render-drr]].- Segmentation is CPU/GPU mixed and may take several minutes depending on volume size.
Prerequisites
- [[i4h-catheter-navigation-setup]] completed (imports and CLI work).
- A CT NIfTI and matching vessel segmentations (or TotalSegmentator subject).
-
= 32 GB RAM recommended for large volumes.
Limitations
- Does not ship data; user must download or provide their own CT.
- Zenodo mirror is throttled; prefer the Dropbox URL in Step 2.
Troubleshooting
- Error:
SUBJunset or missingct.nii.gz- Fix: download Step 2 dataset or setSUBJto an existing subject path. - Error: segment_vessels fails on
--ts-gt-dir- Fix: confirm${SUBJ}/segmentationsexists (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.
What ships with it: 4 files
17.6 KB alongside SKILL.md
evals/
- evals.json5.3 KB
- BENCHMARK.md4.0 KB
- skill-card.md3.9 KB
- skill.oms.sig4.5 KB