2. Usage

2.1. Annotation Mode

2.1.1. Launch napari

napari

2.1.2. Load dataset

1. Plugins > napari-PHILOW > Annotation Mode

2. Select original dir

Note

All slices must be in separate PNG and must be sequentially numbered (e.g. 000.png, 001.png …).

3. Select mask dir

Note

To resume from the middle of the annotation, specify here the name of the directory containing the mask image. The directory must contain the same number of files with the same name as the original image. If you are starting a completely new annotation, you do not need to specify a directory. The directory for mask is automatically created and blank images are generated and stored.

4. Enter a name for the label or model you want to create

Note

This name will be used as the directory name of the newly created mask dir if no mask dir is specified, and as the name of the csv file for training dataset management.

5. Check if you want to create new dataset (new model)

Note

When checked, if there is already a csv file for training dataset management, a new csv file with one sequential number will be generated.

6. Start tracing

2.1.3. Create labels

Note

Create a label with the brush function. more information → https://napari.org/tutorials/fundamentals/labels.html

2.2. Training Mode

2.2.1. Launch napari

napari

2.2.2. Load dataset

1. Plugins > napari-PHILOW > Trainer

2. Select original dir

Note

All slices must be in separate PNG and must be sequentially numbered (e.g. 000.png, 001.png …).

3. Select labels dir

Note

All label images should be named same as original images and contains data management csv file.

4. Select dir for save trained model

2.2.3. Train the model

1. Click on the “start training” button

2. Dice loss score and the predicted results are displayed.

Note

For more detail, check the command line for the progress of training. If you want to stop in the middle, click stop button.

2.3. Prediction Mode

2.3.1. Launch napari

napari

2.3.2. Load dataset

1. Plugins > napari-PHILOW > Predicter

2. Select original dir

Note

All slices must be in separate PNG and must be sequentially numbered (e.g. 000.png, 001.png …).

Note

Select labels dir if you want to keep labels which were used on training, and data management csv file

3. Select model dir contains hdf5 file

4. Select output dir for predicted labels

5. Uncheck the box if you DO NOT want to use TAP (Three-Axes-Prediction)

2.3.3. Predict using the model

1. Click on the “predict” button

Note

Check the command line for the progress of prediction. If you want to stop in the middle, use ctrl+C.

Note

You can start the next round of annotation by selecting the merged_prediction directory as the mask dir in Annotation mode.