Anet plugin for ImageJ

Process images with pre-trained models for ANNA-PALM, using Tensorflow-java backend.

For more details, please checkout the ANNA-PALM project website and also this repository.

Installation

• Download the latest Anet-ImageJ plugin from the releases.
• Copy the .jar file into your ImageJ/FIJI's plugin folder, or directly drag the .jar file into ImageJ (then restart ImageJ).
• You will find an A-net entry in Plugins of ImageJ menu.

Usage

• Click Setup A-net in the ImageJ Plugins menu, click the download button and wait until you see a list of models.
• select a model and click ok.
• Open your image, notice that your image size must match the model input, different model can have different input image size. By default, built-in models has the input size of 512x512. If your input image is bigger or smaller than that, you will need to crop or pad with zeros manually in ImageJ.
• Click Run A-net, select input images, and then click ok
• Wait for a while, you should be able to see the result in a pop image window.

Sample images can be downloaded from here: STORM image 1000 frames (used as figure 4 in the paper), cropped to 512x512, full size version (2560x2560), more sample images can be exported from https://annapalm.pasteur.fr.

Use your own model

A set of pre-trained models are provided, a shortcut to download these models is to use the download button in the plugin. You can check directly in a folder named anet-models in your ImageJ folder (next to the plugin folder). You can also download a zip containing the models manually from here.

Model files for the plugin consist of two files: a tensorflow_model.pb file for the frozen model, and a config.json file for the configuration which describe the GUI, input and output of the model.

You can create a new folder (e.g. named "mito_model_v2.3") inside the anet-models folder in ImageJ. Then place your own model file(tensorflow_model.pb) generated with the freeze.py script in ANNA-PALM. To define the GUI and describe the model, you need to manually add a config.json file next to your model file tensorflow_model.pb. To simplify the process, you can just copy an existing config.json file provided by us. In case you have a different input image size, you will need to replace all the 512 into your own size in config.json.

Once you're done, run Setup A-net from the menu and you should be able to see your model shown in the model list. Then select it and process images with your own model.

Citation

Please cite our paper: Ouyang et al., Nat. Biotechnol. 2018, doi:10.1038/nbt.4106