---

name: segmentation-workflows description: "Operate MedicalZooPytorch 3D segmentation model selection, training, checkpointing, TensorBoard logging, and inference/visualization workflows." metadata: disco-role: operating disable-model-invocation: true license: MIT

Segmentation workflows

Use this sub-skill for the 3D segmentation branch of MedicalZooPytorch: model selection, model construction, training loops, checkpointing, TensorBoard logging, and inference/visualization.

What this sub-skill covers

• Model factory routing through lib.medzoo.create_model(args).
• The common model base in BaseModel for checkpoint save/restore and inference.
• The bundled training loop in lib.train.Trainer and the generic scaffold in lib.train.BaseTrainer.
• TensorBoard and CSV logging through lib.visual3D_temp.TensorboardWriter.
• Non-overlap inference and 3D volume visualization helpers in lib.visual3D_temp.viz.
• 3D model families: UNET3D, VNET, VNET2, DENSENET1, DENSENET2, DENSENET3, HYPERDENSENET, SKIPDENSENET3D, DENSEVOXELNET, RESNET3DVAE, RESNETMED3D, and HIGHRESNET.

Fast path

1. Choose a model family and a valid channel count for that family.
2. Build the model with lib.medzoo.create_model(args).
3. Create the loss, loaders, and trainer.
4. Train, checkpoint, and monitor scalars.
5. Use the visualization helpers for patch-based inference and saved volumes.

Entry points

• references/model-overview.md for the supported 3D model families and factory behavior.
• references/workflows.md for training, checkpointing, TensorBoard, and inference patterns.
• references/troubleshooting.md for known model- and workflow-level pitfalls.
• scripts/smoke_model_factory.py for a safe factory-and-forward smoke check.
• scripts/smoke_writer.py for a temp-directory TensorBoard writer smoke check.

Operating notes

• The factory uses uppercase model ids and asserts membership in its model list.
• HYPERDENSENET selects the 2-channel or 3-channel variant from inChannels.
• DENSENET2 and DENSENET3 are multi-stream models; match the input channel count to the branch you want. The bundled factory smoke skips DENSENET2 by default because the source channel math is known to fail there.
• RESNETMED3D is wired through the factory at depth 18; use the direct generator if you need a different depth.
• Some forward methods return tuples. Unpack them before you compute losses or log shapes.
• The bundled factory smoke has a --include-known-broken flag if you need to reproduce the DENSENET2 channel-math failure explicitly.
• The legacy inference demo depends on a checkpoint and GPU-style tensor placement; treat it as reference-only.