2D and 3D Arabidopsis segmentation - BioImage.io

Authors

A. Wolny , Lorenzo Cerrone, Qin Yu

Description

This is a set of models trained to predict the cell boundaries in confocal stacks of different parts of Arabidopsis : ovules, lateral root primordium and apical stem cells.

We have 2D models (slice by slice, smaller and faster models) and 3D models (slower but more accurate).

• Input channel: confocal stacks, grayscale

• Scale:

  • 2D and 3D Unet Arabidopsis Ovules Cells, 0.235 µm x0.150 µm x 0.150 µm

  • 3D Unet Arabidopsis Ovules Nuclei, 0.35 µm x0.1 µm x 0.1 µm

  • 2D and 3D UNet Arabidopsis Apical Stem, 0.25 µm x0.25 µm x 0.25 µm

  • 3D Unet Lateral Root Primordium, 0.25 µm x 0.1625 µm x 0.1625 µm

• Bit depth: 8-bit or 16-bit

• Output channel:

  • For cell boundaries: probability of cell boundaries

  • For nuclei: probability of nuclei

This model was downloaded and converted from BioImage.io, respecting the associated license (see License section below for more information)

Download

By downloading, installing, copying, accessing, or using the software, you agree to the terms of this end user license agreement.

• Model files 0011-0016 (.zip)

• Test images (.zip)

Download model files with test images (ZIP archive)

0011-0016-3D_Unet_arabidopsys_group.zip

Download ZIP • 219.94MB

Requirements

Make sure you have installed Aivia and the required DeepLearning module (according to our Wiki).

Installation and apply instructions

Aivia is required for applying the model file. You can request a demo copy of Aivia here.

1. Drag-and-drop the model file into the Recipe Console area; or use the 'Load recipe' option in the Recipe Console to load the model file.

2. Load the test image (or any image of your own) into Aivia.

3. If your image contains more than one channel, click on the 'Input & Output' section and specify the image channel you wish to apply the model on.

4. Click 'Start' to apply the model.

License

Copyright 2023, A.Wolny, L. Cerrone . MIT License. Full license information can be found here.

Acknowledgement

Bioimage.io is supported by AI4Life. AI4Life has received funding from the European Union's Horizon Europe research and innovation program under grant agreement number 101057970.

About AI4Life: https://ai4life.eurobioimaging.eu/.

References

Wolny, Cerrone et al. Accurate and Versatile 3D Segmentation of Plant Tissues at Cellular Resolution. eLife 2020. https://elifesciences.org/articles/57613