napari-unet-assistant 0.6.0

a napari plugin for TIFF-based 2D and 3D U-Net segmentation workflows.

napari-unet-assistant

napari-unet-assistant is a napari plugin for supervised 2D and 3D U-Net segmentation workflows.

It is designed for users who already have image-mask training data and want to pair files, train a U-Net model, run inference, and inspect results directly inside napari.

This plugin is separate from SAM-based annotation workflows. Its focus is conventional supervised U-Net training from existing image-mask pairs.

What's new in 0.6.0

• Added recursive dataset-folder auto pairing for TIFF datasets with images/masks, raw/labels, mixed-folder, and nested layouts.
• Added folder-name role hints so dataset folders can guide image-mask pairing even when filenames are less explicit.
• Added configurable augmentation presets: none, conservative, balanced, and strong.
• Added custom augmentation controls for flips, rotation, shear, scale, brightness jitter, and Gaussian noise.
• Added model-capacity options for standard, large, and xlarge U-Net widths.
• Added a model registry with built-in U-Net, MONAI, nnU-Net, and segmentation-models-pytorch backend hooks.
• Added separate model-family, backbone/encoder, and encoder-weight controls for clearer U-Net variant testing.
• Added a Model Sweep tab for running multiple backend, model-family, backbone, weight, and capacity combinations sequentially.
• Added saving and loading of augmentation settings in run configuration metadata.
• Added a training stop button that cancels after the current batch, discards the interrupted model state, and clears GPU cache when available.
• Added a U-Net architecture preview for the selected 2D/3D mode and output-channel configuration.
• Added short tooltips across the main controls to explain settings in place.
• Improved the training UI layout with clearer tabs and more stable dock-widget sizing.

Highlights

• TIFF-first 2D and 3D U-Net training
• Binary and multiclass segmentation
• Recursive smart image-mask pairing
• Pair review with confidence, reason, and shape-check status
• Patch-based training with configurable patch size and overlap
• Optional empty-mask patch inclusion
• Configurable augmentation presets and custom augmentation controls
• Standard, large, and xlarge U-Net capacity options
• Optional model backends for MONAI, nnU-Net, and segmentation-models-pytorch
• Model Sweep for batch training and validation comparison across model choices
• 80/20 validation split
• Continue training from a previous run
• Training cancellation from the UI
• Single-image and folder inference
• 2D image and 3D volume prediction
• TIFF prediction export
• napari-based visualization and QC

Smart image-mask pairing

The plugin can pair training data from:

• a dataset root scanned recursively
• separate image and mask folders
• one mixed folder containing both images and masks
• a manual CSV file

Dataset-root auto scan supports common layouts such as:

• images/ + masks/
• raw/ + labels/
• nested TIFF folders under one dataset root
• one mixed folder containing sample.tif + sample_mask.tif

Supported naming patterns include:

• sample.tif + sample_mask.tif
• sample_1.tif + sample_2.tif
• sample_image.tif + sample_mask.tif
• sample_raw.tif + sample_label.tif

After scanning, the plugin shows each proposed pair with confidence, reason, and shape-check status. Ambiguous or invalid pairs are reported instead of being silently used for training.

Folder names such as images, raw, masks, and labels can also provide role hints during recursive dataset scans.

Augmentation

Training supports augmentation presets and custom controls:

• none: no augmentation
• conservative: flips, small rotations/scales, and light brightness jitter
• balanced: stronger rotation/scale, shear, brightness jitter, and light Gaussian noise
• strong: wider rotation/scale/shear ranges, stronger brightness jitter, and stronger Gaussian noise
• custom: user-selected flips, rotation, shear, scale, brightness, and noise settings

The selected augmentation configuration is saved in each run folder's config.json and restored when loading run metadata.

Training controls

The training panel includes a stop button for cancelling an active training run. Cancellation is checked between batches, so the current batch may finish before the run stops.

When a run is stopped, the interrupted model state is discarded and GPU cache is cleared when available.

Model capacity

Training can use standard, large, or xlarge U-Net widths. For 2D models, these use base channel widths of 32, 64, and 128. Larger models can learn more complex boundaries, but they need more GPU memory and may require a smaller batch size.

Model backends

The default backend is the built-in U-Net and works without extra model packages. MONAI, segmentation-models-pytorch, and nnU-Net are optional backends, so they must be installed into the same Python environment that runs napari. Prefer python -m pip from that environment so the package is installed where napari can import it.

If you install a release of napari-unet-assistant that declares the optional extras, you can use:

python -m pip install napari-unet-assistant[monai]
python -m pip install napari-unet-assistant[smp]
python -m pip install napari-unet-assistant[nnunet]
python -m pip install napari-unet-assistant[models]

Use models to install all optional model backends at once. If your shell treats square brackets specially, quote the requirement:

python -m pip install "napari-unet-assistant[models]"

If the extras command does not install the backend package, install the backend dependency directly:

python -m pip install monai
python -m pip install segmentation-models-pytorch
python -m pip install nnunetv2

For a development checkout, install the extras from the repository root:

python -m pip install -e ".[models]"

For the GitHub version, put the extras before the @ URL:

python -m pip install "napari-unet-assistant[models] @ git+https://github.com/wulinteousa2-hash/napari-unet-assistant.git"

You can verify that the optional packages are available with:

python -c "import monai; import segmentation_models_pytorch; import nnunetv2; print('optional model backends ok')"

If this import check fails, napari will still load the built-in U-Net backend, but the missing optional backend will not be usable until its package is installed in the active environment. After installing a backend, restart napari and select it from the Model backend dropdown.

The model registry lives under src/napari_unet_assistant/models/ and separates provider code into providers/. MONAI and segmentation-models-pytorch models are regular torch.nn.Module backends. nnU-Net is reserved as a pipeline adapter because nnU-Net manages its own data conversion, training, and prediction workflow.

Model selection is split into:

• Model backend: implementation source, such as built-in, MONAI, SMP, or nnU-Net
• Model family: architecture family, such as U-Net, U-Net++, SegResNet, or DeepLabV3+
• Backbone / encoder: feature extractor when the selected family supports one, such as ResNet34, ResNet50, EfficientNet-B0, DenseNet121, or MobileNetV2
• Encoder weights: pretrained encoder weights when supported
• Model capacity: built-in width preset used by backends that expose width/depth-style capacity

Model Sweep

Use the Model Sweep tab when you want to compare multiple model configurations without starting each run manually. Set the shared training constants first, including data shape, task type, patch settings, augmentation, validation split, epochs, and batch size. Then select the backends and capacities to test, click Preview model sweep, and click Run model sweep.

The sweep runs configurations sequentially so one GPU is used predictably. Each completed configuration gets its own run folder with the usual files:

• config.json
• run_summary.txt
• validation.json
• history.csv
• summary.json
• best_model.pt

The sweep folder also writes:

• sweep_config.json: shared settings, augmentation, selected configurations, and skipped entries
• sweep_results.csv: one summary row per skipped, failed, or completed configuration
• pairs.csv: image-mask pairs used by the sweep

The sweep_results.csv table includes backend, model family, capacity, encoder, encoder weights, best validation loss, best validation Dice/IoU/F1, run folder, and any error message. Failed combinations do not stop the sweep; they are recorded and the next configuration runs. The stop button cancels after the current batch or epoch boundary and leaves partial results on disk.

The current nnU-Net entry is reported as skipped in Model Sweep because nnU-Net uses its own data-conversion, training, and prediction pipeline rather than the plugin's direct torch.nn.Module training loop.

Manual CSV pairing

For manual pairing, provide a CSV file with one image-mask pair per row.

Required columns:

• image_path
• mask_path

Optional column:

• key

Example:

key,image_path,mask_path
sample01,/path/to/images/sample01.tif,/path/to/masks/sample01_mask.tif
sample02,/path/to/images/sample02.tif,/path/to/masks/sample02_mask.tif

Use absolute paths for the clearest behavior. Relative paths are interpreted from the current working directory.

Each image and mask should have matching spatial dimensions.

Installation

pip install git+https://github.com/wulinteousa2-hash/napari-unet-assistant.git

For editable development:

git clone https://github.com/wulinteousa2-hash/napari-unet-assistant.git
cd napari-unet-assistant
pip install -e .
napari

Basic workflow

1. Open napari.
2. Launch U-Net Assistant.
3. Choose a pairing mode.
4. Scan and review image-mask pairs.
5. Set training options.
6. Train a 2D or 3D U-Net model.
7. Load a saved run folder.
8. Run inference on new images or volumes.
9. Review prediction masks in napari.

Supported data

2D training

• image: (Y, X) grayscale TIFF
• mask: (Y, X) label TIFF
• binary masks: 0 = background, nonzero = foreground
• multiclass masks: integer labels

3D training

• image: (Z, Y, X) grayscale TIFF
• mask: (Z, Y, X) label TIFF
• multiclass masks should use integer labels:
  • 0 = background
  • 1 = class 1
  • 2 = class 2
  • 3 = class 3

Patch options

2D

XY patch sizes:

• 64
• 128
• 256
• 512
• 1024

Default: 256 x 256

3D

Z patch sizes:

• 8
• 16
• 32
• 64

XY patch sizes:

• 64
• 128
• 256
• 512
• 1024

Default: 16 x 256 x 256

Validation

The current training workflow uses a standard train/validation split. The default validation split is 20%.

K-fold cross-validation is not active in this release.

Each run writes validation.json with the active validation mode, split fraction, random seed, total patch count, train patch count, and validation patch count. The training log also reports the same split. Per-epoch validation metrics are written to history.csv.

At training start, the log reports the model backend, model family, backbone/encoder, encoder weights, capacity, patch settings, augmentation preset, validation setup, epochs, and batch size. The same user-readable summary is written to run_summary.txt, while full structured settings are saved in config.json.

Outputs

Each run folder can contain:

• best_model.pt
• config.json
• run_summary.txt
• summary.json
• history.csv
• validation.json
• pairs.csv
• prediction TIFF outputs

Current scope

This release is focused on TIFF-based supervised U-Net training and inference.

OME-Zarr, spectral/lambda workflows, and SAM-assisted annotation are intentionally outside the scope of this version.

Reference

This project builds on U-Net-based nerve morphometry workflows described in:

Moiseev D, Hu B, Li J. Morphometric Analysis of Peripheral Myelinated Nerve Fibers through Deep Learning. Journal of the Peripheral Nervous System. 2019;24(1):87-93.
https://pmc.ncbi.nlm.nih.gov/articles/PMC6420354/