NeuroSeg2

This is an implementation of NeuroSeg-II on Python 3, Keras, and TensorFlow.

The repository includes:

Source code of NeuroSeg-II built on FPN and ResNet.
Training code for Neurofinder.
Testing code for Neurofinder.
Testing code for mesoscopic two-photon calcium imaging.
Code of preprocessing.

Using NeuroSeg-II

Create a new environment

environment.yaml supports the normal running of NeuroSeg-II. Before using NeuroSeg-II, ensure that the environment is configured according to this file.

conda env create -f environment.yaml

Dataset preparation

In this folder Neurofinder, We provide two images for testing.

• In this folder, leftImg8bit stores the two-photon calcium imaging and gtFine stores the corresponding GT.
• generate_dataset.py is used to generate image list. After adding new images, run this code to generate the list for training and test code can read new images.

Model preparation

This folder models is used to store the pretrained model and the test model. The test model can be downloaded from our huggingface.

training or testing

After abtaining the dataset and model, running test.py to test the image.
Running train.py to train the new dataset.

The result

logs/evalution contains the results of the neurons segmentation of NeuroSeg-II.

• plt/difference stores the segmented image by NeuroSeg-II.
• evaluation_log.csv is the score for this test.

Other matters

Core code

In neuroseg2 are the core code of NeuroSeg-II.

• model.py and utils.py are the code of overall structure.
• Down.py is the code of FPN+.
• attention.py is the code of attention mechanism.
• visualize.py is the code for visual segmentation result.

Code of preprocessing

In utilities are the code for preprocessing.

Contact information

If you have any questions about this project, please feel free to contact us. Email address: zhehao_xu@qq.com