napari-labelprop-remote

3D semi-automatic segmentation using deep registration-based 2D label propagation

Installation

Server

Install LabelProp

To install it with CUDA 11.8 :

git clone https://github.com/nathandecaux/labelprop
cd labelprop
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install -e .

Client

First, install napari:

pip install napari[all]

Then, to install the napari plugin :

git clone https://github.com/nathandecaux/napari-labelprop-remote.git
cd napari-labelprop-remote
pip install -e .

Usage

Server

Start the server with the following command :

labelprop launch-server [--addr,-a`<HOST>`] [--port,-p `<PORT>`]

This will start a Flask web server on the host <HOST> and port <PORT>. The default values are 0.0.0.0 and 5000.

Client

Setup

Start napari and open the plugin with the following command :

napari

Then, reach the plugin in the menu bar :

Plugins > napari-labelprop-remote > Configure

Fill the fields with the host and port of the server. Set localhost as host if you have set the server in the same machine. Then, click on the Configure Server button. Once the server is configured, you will be able to set the server-side checkpoint directory. This is the directory where the server will save the checkpoints. The default value is path/to/labelprop/checkpoints

Training

To train a model, reach the plugin in the menu bar :

Plugins > napari-labelprop-remote > Training

Fill the fields with the following information :

• image : Select a loaded napari.layers.Image layer to segment
• labels : Select a loaded napari.layers.Labels layer with the initial labels
• hints : Select a loaded napari.layers.Labels layer with scribbled pseudo labels
• pretrained checkpoint : Select a pretrained checkpoint from the server-side checkpoint directory
• shape : Set the shape of slices to use for training and inference
• z axis : Set the axis to use for the propagation dimension
• max epochs : Set the maximum number of epochs to train the model
• checkpoint name : Set the name of the checkpoint to save on the server-side checkpoint directory
• criteria : Defines the criteria used to weight each direction of propagation ncc = normalized cross correlation (slow but smooth), distance = distance to the nearest label (fast but less accurate)
• reduction : When using ncc, defines the reduction to apply to the ncc map mean / local_mean / none. Default is none
• gpu : Set if whether to use the GPU or not. Default is True (GPU). GPU:0 is used by default. To use another GPU, set the CUDA_VISIBLE_DEVICES environment variable before launching napari.

Then, click on the Run button. The training will start and the progress will be displayed in the server console. Once the training is done, the checkpoint will be saved on the server-side checkpoint directory. Napari will display the forward (propagated_up) and backward (propagated_down) propagated labels and the fused labels (propagated_fused).

Inference

To run inference on a model, reach the plugin in the menu bar :

Plugins > napari-labelprop-remote > Inference

Fill the fields like in the training section. Then, click on the Run button.

Set label colormap

To set the colormap of the labels, reach the plugin in the menu bar :

Plugins > napari-labelprop-remote > Set label colormap

Let you load a Label Description file from ITKSNAP and set it to the highlighted label layer.

Demo (inference)