Chi Zhang, Qihua Chen, Xuejin Chen. Self-Supervised Learning of Morphological Representation for 3D EM Segments with Cluster-Instance Correlations. In MICCAI 2022
cd fafb-cellseg
uzip *.zip
The segment ids of corresponding classes are in glias/neurites/soma.txt. You can copy the ids and visualize them in Neuroglancer.
The point cloud data are in the folders, where each line records the coordinates of a point.
/*Note that we expanded the number of glia in the dataset to 331 (in the paper we have 110).
Feature embedding space of MorphConNet on FAFB-CellSeg.
The glia segments we annotated in the fly brain:
uzip ./code/pretrained/model_sslcontrast.pth.zip
Finetune the model with the classification task of fafb-CellSeg: finetune_eval.ipynb
Linear classification and t-sne visualization: linear_feature_eval.ipynb
Format your dataset as:
your_dataset_forlder
├── neuron_dataset_shape_names.txt # contains the class_name
├── neuron_dataset_test.txt # contains the file names in folder class_name
├── class_name
├── your_point_cloud_data1.txt
├── your_point_cloud_data2.txt
├── ...
Modify the dataset path in ./main.py
Config the training parameters in ./config/config.yaml
python main.py