Training your own DeepD3 model
Use deepd3-training to start the GUI for generating training sets.
For each of your training set, please provide
The original stack as e.g. TIF files
The spine annotations (binary labels) as TIF or MASK files (the latter from [pipra](https://github.com/anki-xyz/pipra))
The dendrite annotations as SWC file (only tested for SWC-files generated by [NeuTube](https://neutracing.com/download/))
Create training data
Click on the button “Create training data”. For each of your stacks, import the stack, the spine annotation and the dendrite annotation file. If you dendrite annotation is a SWC file, it will create a 3D reconstruction of the SWC file, which will be stored for later use. If you reload the SWC, it will ask you if you want to keep the 3D reconstruction.
After importing all files, enter the metadata (resolution in x, y and z) and determine the region of interest using the bounding box and the sliders.
Shortcuts are B for current plane is z begin and E for z end. You may enable or disable the cropping to the bounding box. If you are happy, save this region as d3data-file.
View training data
Click on the button “View training data” to re-visit
any d3data files. You also are able to see and potentially
manipulate the metadata associated the d3data file.
Arrange training data
For training, you need to create a d3set.
This is an assembly of d3data files. Click on the button
“Arrange training data”. Then, simply load all relevant data using
the “Add data to set” button and select appropriate d3data files.
Clicking on “Create dataset” allows you to save your assembly
as d3set file.
Actual training
We have prepared a Jupyter notebook in the folder examples.
Follow the instructions to train your own deep neural network for DeepD3 use.
For professionals, you also may utilize directly the files in model
and training to allow highly individualized training.
You only should ensure that your model allows arbitrary input and
outputs two separate channels (dendrites and spines).