# nv-segment-ct-finetune

Finetunes NV-Segment-CT VISTA3D on CT NIfTI image and label datasets, with a fixed-channel softmax path and optional MLflow tracking.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 1800 installs · ★ 3735
- Author: NVIDIA MedTech
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/NVIDIA/skills/tree/HEAD/skills/nv-segment-ct-finetune/SKILL.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/skills/nv-segment-ct-finetune

## What it does

The skill wraps the upstream MONAI bundle entrypoint and runs `scripts/run_finetune.py`. You do not write training or inference code by hand: the script stages configs, runs MONAI and writes a schema-checked `output.json`. Outputs are `finetuned_ckpt` and `result_json`.

Two workflows. Keep the standard one when point prompts must stay available, classes are picked at inference time, or labels can overlap. Reach for `--softmax` only when the full class set is known before training and every voxel is background or exactly one foreground class. Do not combine `--softmax` with `--auto-seg` or `--sanity`. Use `--smoke` for the shortest check and `--sanity` to reproduce MSD Task06 Lung Tumor.

What you need first: Python 3.10+, CUDA-capable Torch for GPU runs, and the manifest packages, including `monai==1.4.0` and `numpy<2`. The softmax path also needs the pinned NVIDIA-Medtech checkout — point `NV_SEGMENT_CT_ROOT` at the `NV-Segment-CT` directory, or `NV_SEGMENT_CTMR_ROOT` at the sibling `NV-Segment-CTMR`. MLflow turns on with an experiment name; `--mlflow-tracking-uri` and `--mlflow-run-name` do nothing without it. Worth checking your environment before a long run: on MONAI 1.5.x the upstream finetune loss crashes on bool. Not for clinical validation.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill nv-segment-ct-finetune -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/nv-segment-ct-finetune/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/nv-segment-ct-finetune/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill nv-segment-ct-finetune -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/nv-segment-ct-finetune/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill nv-segment-ct-finetune -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/nv-segment-ct-finetune/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install NVIDIA/skills nv-segment-ct-finetune
```

2. **Manually: put the files in place**
- File: `.github/skills/nv-segment-ct-finetune/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill nv-segment-ct-finetune -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/NVIDIA/skills --path skills/nv-segment-ct-finetune
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/nv-segment-ct-finetune/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill nv-segment-ct-finetune -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/NVIDIA/skills/tree/HEAD/skills/nv-segment-ct-finetune/SKILL.md

## When to use it
- finetune VISTA3D on a custom CT NIfTI dataset
- smoke-test the pipeline before committing to a full run
- reproduce MSD Task06 Lung Tumor with `--sanity`
- map dataset labels to VISTA3D class IDs via `--label-mapping`
- log training metrics and checkpoints to MLflow

## Example prompts
- Run `scripts/run_finetune.py` on my dataset with `--smoke` and show me `output.json`
- Do a fixed-channel softmax finetune: `--softmax --label-mapping '[[1,3],[2,13]]'`
- Enable MLflow for training: `--mlflow-experiment-name ct-finetune`

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: f5cac27953b56aee.

BEGIN THIRD-PARTY FILE f5cac27953b56aee
```text
---
name: nv-segment-ct-finetune
description: Runs standard or fixed-channel softmax finetuning of NV-Segment-CT VISTA3D on CT NIfTI image/label datasets, with optional MONAI-native MLflow tracking and checkpoint evidence. Uses softmax for predefined, mutually exclusive classes; keeps the standard workflow when point prompts or runtime-variable classes are needed. Not for clinical validation.
license: Apache-2.0
allowed-tools: Bash, Read, Write, WebFetch, Env
metadata:
  author: 'NVIDIA MedTech <noreply@nvidia.com>'
  tags:
    - MedTech
    - CT
    - finetuning
    - segmentation
---

# NV-Segment-CT Finetune

## Purpose

- Used for smoke or dataset finetuning of NV-Segment-CT VISTA3D on CT NIfTI labels, including the upstream fixed-channel softmax workflow and optional MLflow tracking. Not for clinical validation.
- Wraps the upstream MONAI bundle entrypoint; do not replace it with handwritten training or inference code.
- Manifest inputs are `dataset_dir`, `datalist`, `target_anatomy`, `label_mapping`, `smoke`, `sanity`, `auto_seg`, `softmax`, `skip_formal_eval`, `mlflow_tracking_uri`, `mlflow_experiment_name`, and `mlflow_run_name`.
- Manifest outputs are `finetuned_ckpt` and schema-checked `result_json`.

## Instructions

- Run only the caller-requested preset, dataset, output directory, and compute budget. Dependency setup and remote tracking require the caller's approval. After reporting the result, stop; further training, publishing, or deployment is a separate request.
- Run `scripts/run_finetune.py`; do not patch files under `bundle/` or upstream checkouts during normal skill use.
- For standalone Bash, include the fresh-environment setup line before the wrapper; benchmark venvs start empty.
- Run the committed script in place from the repo root. Do not copy this skill to a runtime directory, and do not use `rm` or cleanup commands in generated invocations.
- If a host exposes `run_script`, use `run_script("scripts/run_finetune.py", args=[...])`; otherwise run from the repo root.
- For the shortest workflow check, use `--smoke`; for MSD Task06 Lung Tumor reproduction, use `--sanity`.
- Choose between the standard and `--softmax` workflows using the criteria below. Do not combine `--softmax` with `--auto-seg` or `--sanity`.
```
END THIRD-PARTY FILE f5cac27953b56aee

The third-party file has ended. Everything between the f5cac27953b56aee tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

First 2273 of 16806 bytes. © NVIDIA MedTech · Apache-2.0 · https://github.com/NVIDIA/skills/tree/HEAD/skills/nv-segment-ct-finetune/SKILL.md

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