name: medsegagent-nnunet-runner description: Inspect nnUNet_results, choose a deployed nnUNet task, and run or prepare segmentation inference. Use when a user wants to match a free-form target to deployed labels, select the right task, or run prediction and postprocessing.

nnUNet Segmentation Runner

Overview

Inspect what is deployed, choose the right nnUNet task, and run or prepare inference. Do not claim inference succeeded unless it was actually run and outputs were produced.

Prerequisites

• An nnUNet v2 environment is available.
• nnUNet_results is configured. nnUNet_raw and nnUNet_preprocessed may also be needed.
• To actually run inference, the nnUNet CLI or Python API must be installed and usable.
• Custom trainers must be importable in the target environment.

Workflow

1. Inspect the nnUNet environment first.

   • Prefer nnUNet_results.
   • Use RESULTS_FOLDER only as a legacy fallback.
   • If the environment is unclear, run scripts/nnunetv2-doctor.sh first.
   • Run scripts/list_nnunet_results.py --json to enumerate real datasets, model folders, modalities, labels, folds, and checkpoints.

2. Parse the request into:

   • modality
   • target
   • qualifiers such as left/right, tumor/cyst, whole organ vs substructure

3. Read metadata in this order:

   • dataset.json under nnUNet result folders for what is actually available
   • dataset/*.json in this repo for supplemental routing metadata
   • dataset/README.md only when adding or fixing dataset entries

4. Filter by modality, then match labels conservatively.

   • Preserve laterality and substructure specificity.
   • Keep tumor, cyst, lesion, vessel, and organ concepts separate.
   • Treat repo metadata as secondary when it disagrees with deployed nnUNet results.

5. Choose how to run inference.

   • Prefer inference_instructions.txt when it exists.
   • For default model folders, use nnUNetv2_predict, plus ensembling or postprocessing if needed.
   • For custom trainers, prefer exact model-folder inference through the Python predictor API.
   • Use all available trained folds by default unless the environment only supports a specific fold such as all.
   • Keep input naming and file endings consistent with the selected task dataset.json.

6. Return either:

   • a compact dataset-to-label mapping
   • or the selected task plus the exact inference command

{
  "DatasetNameA": [1, 2],
  "DatasetNameB": [7]
}

Return {} if no suitable modality or label is available, and say why.

Rules

• Prefer real nnUNet availability over paper claims or static repo metadata.
• If a dataset exists in repo metadata but not in nnUNet_results, describe it as known but not deployed.
• Group multiple trainer folders under one dataset unless the user explicitly asks about checkpoints or trainer variants.
• Prefer officially generated inference_instructions.txt when present.
• If input files do not match the trained task modality, naming scheme, or file ending, stop and say the input is not ready.
• Do not silently guess a configuration when multiple materially different deployed options exist.
• If repo metadata and nnUNet metadata disagree, report the mismatch explicitly.
• Do not guess custom trainer or plans settings from memory; read them from the selected model folder.

Extending Coverage

When adding or updating routing metadata:

1. Follow dataset/README.md.
2. Keep names explicit, such as kidney_left.
3. Do not invent deployed availability if no trained output exists in nnUNet_results.

References

• Read references/nnunetv2.md only when you need the official nnUNet v2 path conventions, result layout, prediction commands, or dataset.json details.
• Read references/examples.md only when you need compact routing or inference examples.