Nv Segment Ct Finetune

NV-Segment-CT Finetune

Purpose

• Used for smoke or dataset finetuning of NV-Segment-CT VISTA3D on CT NIfTI labels. 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, and skip_formal_eval.
• Manifest outputs are finetuned_ckpt and schema-checked result_json.

Instructions

• 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.
• Read references/task06-and-results.md only when you need Task06 reference details, output-field definitions, or manual bundle setup notes.

Available Scripts

Prerequisites

• Python 3.10+ with CUDA-capable Torch for GPU runs.
• Runtime packages from skill_manifest.yaml, especially monai==1.4.0, numpy<2, nibabel, scipy, typer, PyYAML, fire, pytorch-ignite, einops, and huggingface_hub.
• Optional environment variables: CUDA_VISIBLE_DEVICES restricts visible GPUs; NPROC_PER_NODE overrides GPU count and values >=2 select multi-GPU mode for non-sanity runs.
• Side effects: writes generated bundle configs under skills/nv-segment-ct-finetune/bundle/configs/, including skills/nv-segment-ct-finetune/bundle/configs/auto_override.json, skills/nv-segment-ct-finetune/bundle/configs/train_continual_task06_lung.json, and skills/nv-segment-ct-finetune/bundle/configs/dfw_no_logging.json; writes checkpoints/evidence under --output-dir, may cache model assets under ~/.cache/huggingface/, and may contact https://huggingface.co or https://raw.githubusercontent.com.

Fresh environment setup:

python -m pip install "monai==1.4.0" "numpy<2" pytorch-ignite einops nibabel scipy typer PyYAML fire huggingface_hub

Known upstream compatibility constraints:

• DFW Task06 reference: Python 3.10.16, MONAI 1.4.0, Torch 2.7.0+cu126.
• Use exact monai==1.4.0 for smoke, sanity, and evidence runs; MONAI 1.5.x can crash the upstream finetune loss on boolean labels.
• Do not float the dependency as monai>=1.4,<1.6 in generated commands.

Usage

Smoke-scale workflow check:

python -m pip install "monai==1.4.0" "numpy<2" pytorch-ignite einops nibabel scipy typer PyYAML fire huggingface_hub && \
python skills/nv-segment-ct-finetune/scripts/run_finetune.py \
  PATH_TO_DATASET \
  --smoke \
  --patch-size '[64,64,64]' \
  --output-dir runs/nvseg_smoke

Use the staged dataset as PATH_TO_DATASET. For the micro fixture, use skills/nv-segment-ct-finetune/fixtures/spleen_micro. Smoke mode proves wiring, config generation, checkpoint loading, and runtime compatibility; it is not a quality bar.

MSD Task06 Lung Tumor sanity reproduction:

python skills/nv-segment-ct-finetune/scripts/run_finetune.py \
  /path/to/Task06 \
  --sanity \
  --output-dir runs/nvseg_task06_sanity

The sanity preset follows the single-GPU DFW recipe: fold-0 validation, label mapping [[1, 23]] for lung tumor, automatic class-prompt segmentation, patch [128,128,128], 5 epochs, and original-spacing configs/evaluate.json scoring before and after training. Expected reference range is pretrained Dice about 0.6697, training-best Dice about 0.6905, and fine-tuned formal Dice about 0.6836.

User-data finetune:

python skills/nv-segment-ct-finetune/scripts/run_finetune.py \
  --dataset-dir /path/to/dataset \
  --datalist /path/to/datalist.json \
  --target-anatomy "lung tumor" \
  --auto-seg \
  --epochs 5 \
  --patch-size '[128,128,128]' \
  --output-dir runs/nvseg_user_finetune

Use --label-mapping '[[1, 23]]' when local label values are custom or the anatomy name is ambiguous.

Examples

Smoke run on a staged tiny dataset:

python skills/nv-segment-ct-finetune/scripts/run_finetune.py \
  runs/with_vs_without_nv/_inputs/nv_segment_ct_finetune/input_dataset \
  --smoke \
  --patch-size '[64,64,64]' \
  --output-dir runs/nvseg_smoke

Task06 sanity run on a local MSD cache:

python skills/nv-segment-ct-finetune/scripts/run_finetune.py \
  .workbench_data/datasets/Task06_Lung \
  --sanity \
  --output-dir runs/nvseg_task06_sanity

Data Contract

• Preferred layout: dataset/imagesTr/*.nii.gz and dataset/labelsTr/*.nii.gz.
• Labels must align one-to-one with images by basename.
• The target label value must be present in the training labels.
• Use a datalist when patient-level splitting matters. The bundle default fold is 0, so fold: 0 entries are validation and all other folds are training.
• Every trained foreground label must map to an existing VISTA3D global class id from bundle/label_dict.json; this skill cannot invent a new class.

Results

Check output.json in the run directory first:

• formal_pretrained_val_dice and formal_finetuned_val_dice: original-spacing pre/post scores when formal eval is enabled.
• training_start_val_dice, val_dice_per_epoch, and training_best_val_dice: training-time validation trace.
• finetuned_ckpt_matches_pretrained_weights: detects the epoch-0 checkpoint trap when val_at_start=true.
• recommended_ckpt: checkpoint to keep. Do not blindly use the last epoch or model_finetune.pt.
• runtime.oom, runtime.peak_gpu_mb, and phase logs: distinguish OOM, slow validation, and process failure.

Decision rule: prefer formal original-spacing pre/post scores when present; reject tensor-identical "fine-tuned" checkpoints for sanity recovery; treat improved: false as valid evidence rather than a wrapper failure.

Limitations

• Thin wrapper. Training, validation, transforms, and checkpointing are delegated to the upstream bundle in bundle/.
• Reproduction record only: the successful five-epoch Task06 run used Python 3.12.3, PyTorch 2.12.0+cu130 with CUDA 13.0, MONAI 1.4.0, NumPy 1.26.4, PyTorch-Ignite 0.5.4, NiBabel 5.4.2, SciPy 1.16.0, einops 0.8.2, Fire 0.7.1, Hugging Face Hub 0.36.2, Transformers 4.57.6, Typer 0.25.1, PyYAML 6.0.3, and MLflow 3.14.0 on one NVIDIA RTX 6000 Ada 48 GB GPU. These versions document the evidence environment; they are not additional package constraints or a claim that other versions cannot work.
• The auto-derived plan is heuristic; caller-provided --patch-size, --cache-rate, --epochs, and --learning-rate win.
• The Task06 sanity recipe intentionally forces single-GPU execution to match the DFW reference. Multi-GPU mode for other datasets requires host torchrun support.
• The paired verifier is CPU-only and audits the evidence pack; it does not re-run GPU segmentation.
• Not for clinical deployment, clinical interpretation, autonomous diagnosis, or regulatory submission.

Troubleshooting

Verification

Run the implemented verifier when quality gates matter:

python -m eval_engine.run_trusted skills/nv-segment-ct-finetune \
  --fixture skills/nv-segment-ct-finetune/fixtures/spleen_micro \
  --out runs/nvseg_trusted