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/.
• 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