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EM Segmentation - Wong lab (UW)


This model transforms images acquired on electron microscopy systems to a gray-scale confidence map that corresponds to objects (i.e. cells; bright regions) and their boundaries (i.e. membranes; dark regions). A threshold can be applied to the confidence map to segment the objects and generate object mesh output.


The model is trained on datasets from the Rachel Wong laboratory at University of Washington. It uses the UNet architecture with densely-connected blocks (read about the implementation on ArXiv.org [1]).



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Requirements

Make sure you have Python versions 3.5 to 3.7 installed with TensorFlow-GPU version 1.13.0 and above.


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.


Image credits

Wan-Qing Yu and Rachel Wong, University of Washington


References

  1. S Jégou, et. al. The one hundred layers tiramisu: fully convolutional DenseNets for semantic segmentation. ArXiv.