Sample data are constructed from 3D neuron images of two whole mouse brains (denoted by brain-A and brain-B, respectively), gold standard reconstructions and automatic reconstructions of marked neurons in them. These data were provided by the Southeast University-Allen Institute Joint Center.
Three types of sample data need to be constructed to achieve the tracing difficulty classification: 1. 3D image blocks, gold blocks, and auto blocks; 2. Neuron distance (ND) of gold blocks and auto blocks, and L-Measure (LM) of auto blocks; 3. Annotation data of 3D image blocks.
A model called 3D-SSM is designed to classify the tracing difficultyof 3D image blocks, which is based on ResNet, Fully Connected Neural Net-work (FCNN) and Long Short-Term Memory network (LSTM). 3D-SSMconsists of three modules: Structure Feature Extraction (SFE), Sequence Informa-tion Extraction (SIE) and Model Fusion (MF). SFE utilizes a 3D-ResNet and aFCNN to extract two kinds of features in 3D neuron image blocks and automaticreconstruction blocks. SIE uses two LSTMs to extract sequence information hid-den in features of sequential blocks produced in SFE. MF adopts a concatenationoperation and a FCNN to fuse outputs from SIE.
├── deal_feature Extraction and analysis of L-Measure and neuron distance features.
├── automatic_label Train an FFCNs model and use it to generate automatic labels.
├── classification_model Models are designed to solve the tracing difficulty classification of 3D image blocks.
├── vaa3d_plugin Plug-in for getting 3D image blocks, gold blocks, and auto blocks.
├── annotation.md Annotation relus.
├── README.md
- Get 3D image blocks, gold blocks, and auto blocks using vaa3d_plugin file.
- Get L-Measure and neuron distance features using deal_feature file, and get automatic label using automatic_label file .
- To train and predict the tracing difficulty of 3D image blocks models using classification_model file.