MorphEmbedding

Morphological embedding for single neuron with graph neuron networks.

Data preprocessing

Make sure that the morphology data is composed of ''.swc'' files, each of which contains the morphological structure of a single neuron. Activate your python environment and using:

python SWC2H5PY.py --swc_dir=./neuron7

to generate trainable corresponding point cloud datasets. ''--swc_dir'' is the path where you store the morphological data. Then you will see two ''.h5'' files in your current directory, the training set and the test set.

Train MorphoGNN

When the trainable data is generated, run:

python MorphoGNN.py

to train the MorphoGNN model. After running 50 epoches, the model file named ''MorphoGNN.t7'' appears in the current directory.

Retrieval

''retrieval.py'' helps you retrieve nerve fibers based on the MorphoGNN model you trained. Firstly, you should run:

python retrieval.py --task=ExtractFeature --model_path=./MorphoGNN.t7 --swc_dir=./neuron7

to build a feature library for each neuron, which is saved as ''.npy'' file. Then

python retrieval.py --task=QueryTest --query_times=100

to retrieve the most similar neurons in this library ''100'' times. Or you can using this command:

python retrieval.py --task=Visualize

to visulize features distribution.

Morphometrics

We also provide an example of classifying neurons using sixteen traditional morphometrics in ''morphometrics.py''. Morphometrics are captured through NeuroM. Run:

python morphometrics.py --swc_dir=./neuron7

to genrate datasets with traditional morphometrics and train a simple multilayer perceptron to classify.

Reconstruction Quality Classification

https://github.com/sfwmusi/MorphoGNN_reconcls