---

name: segmentation description: "Guides Attention-Gated Networks medical image segmentation workflows, including 3D U-Net training, validation, NIfTI layouts, and attention or feature-map export." disable-model-invocation: true metadata: disco-role: operating license: MIT

Segmentation

Use this sub-skill for medical image segmentation tasks in Attention-Gated Networks. It covers 2D/3D U-Net variants, CT deep-supervision networks, attention-gated U-Nets, non-local blocks, NIfTI datasets, segmentation training/validation scripts, and feature or attention map exports.

Read first

• Read api-reference.md when you need exact model names, wrapper methods, dataset class signatures, metric functions, or hookable layer names.
• Read data-layout.md before arranging NIfTI data or debugging split, image/label, and transform problems.
• Read workflows.md for training, validation, checkpoint use, and safe attention/feature map export.
• Read troubleshooting.md for CUDA, memory, SimpleITK, torchsample, NIfTI, shape, metric, or layer-hook failures.
• Run run_segmentation.py for skill-owned training commands that replace the source repo's root training entry point.
• Run validate_and_export_maps.py to replace hard-coded validation and feature-map scripts with a parameterized, skill-owned helper.
• For fast environment validation, run the root helper ../../scripts/check_env.py --repo-root /path/to/Attention-Gated-Networks --mode segmentation.

When to use this sub-skill

Use this route when the user asks to:

• train a segmentation network from the repository configs;
• choose between baseline U-Net, non-local U-Net, CT deep supervision, and multi-attention U-Net variants;
• validate a segmentation checkpoint and compute dice, IoU, precision/recall, mean distance, or Hausdorff-style distances;
• arrange 3D NIfTI data for acdc_sax, ukbb_sax, rvsc_sax, or test_sax;
• export NIfTI predictions, attention volumes, feature maps, or slice previews;
• debug 3D shape, memory, CUDA, SimpleITK, torchsample, or layer-hook issues.

Route ultrasound scan-plane classification and 2D attention overlays to ../classification/SKILL.md instead.

Quick workflow

1. Install the repository with CUDA-enabled PyTorch, torchsample, NIfTI, and visualization dependencies. Then run:

   python ../../scripts/check_env.py --repo-root /path/to/Attention-Gated-Networks --mode segmentation
   

2. Arrange NIfTI files under split directories with paired image/ and label/ subfolders. See data-layout.md.

3. Copy a CT config and replace data_path.acdc_sax with the current dataset root. The stock configs use output_nc=4, tensor_dim='3D', and gpu_ids=[0].

Relative config paths are resolved from the explicit --repo-root; relative data_path values are resolved from the config file's parent. NIfTI data and model weights/checkpoints must exist externally. The bundled runner and check_env.py --config ... fail fast on missing or private /vol/... paths; they never fabricate data. 4. Train with the bundled replacement:

python scripts/run_segmentation.py --repo-root /path/to/Attention-Gated-Networks --config path/to/config.json

5. Validate a checkpoint or export maps with the bundled helper:

   python scripts/validate_and_export_maps.py --repo-root /path/to/Attention-Gated-Networks --config path/to/config.json --checkpoint path/to/checkpoint.pth --output-dir /tmp/ag-net-validation --mode validate
   

6. For safe smoke validation or map export without private paths, use:

   python scripts/validate_and_export_maps.py \
     --repo-root /path/to/Attention-Gated-Networks \
     --config configs/config_unet_ct_multi_att_dsv.json \
     --output-dir /tmp/ag-net-segmentation \
     --mode both
   

Key decisions

• CUDA is required for the unmodified source wrappers. A CPU-only install does not verify the selected segmentation workflows.
• unet_ct_dsv is the simpler CT deep-supervision route; use unet_ct_multi_att_dsv when attention maps are a first-class goal.
• Real CT configs use large volumes (160 x 160 x 96 patches). The generated helper's 16 x 16 x 16 synthetic volume is only a wiring smoke.
• The source validation and map-export scripts hard-code private paths. Prefer the bundled helper or explicitly parameterize a project-local copy.
• The source CRF post-processing script is reference-only, not bundled as a safe helper.

Expected outputs

Training writes checkpoints and logs below the configured checkpoint directory and experiment name. Validation writes NIfTI predictions and CSV metrics. The bundled helper writes synthetic or fixture NIfTI outputs, metrics.json, feature/attention-map NIfTI files, and middle-slice preview PNGs to the selected output directory.