NeuroSeg-Ⅲ

NeuroSeg3 is an self-supervised learning approach designed to achieve fast and precise segmentation of neurons in imaging data. This approach consists of two modules: a self-supervised pre-training network and a segmentation network. After pre-training the encoder of the segmentation network via a self-supervised learning method without any annotated masks, we only need to fine-tune the segmentation network with a small amount of annotated data. The segmentation network is designed with YOLOv8s, FasterNet, EMA and BiFPN, which enhanced the model's segmentation accuracy while reducing the computational cost and parameters.

The code of Neuroseg-Ⅲ implements the following functionalities:

• Improve the segment work, based on YOLOv8s, with FasterNet, EMA and BiFPN.
• Training the encoder of the segmentation network via TiCo without any annotated masks.
• Integrating the pre-trained encoder with the segment network for fine-tuning.
• Evaluation of the Neuroseg-Ⅲ framework with standard metrics.

System Requirements

• A CUDA compatible GPU
• Anaconda with Python 3.8
• Pytorch 1.13.1 (CUDA Toolkit 11.6 and cuDNN v8.3.2 required)
• Lightly for self-supervised learning.

You can compile the environment as the following steps:

conda env create -f Neuroseg3_environment.yaml
python setup.py intall
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia

Link to ABO Dataset:

Allen Brain Observatory dataset

Contact information

If you have any questions about this project, please feel free to contact us. Email address: [email protected]