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.
- 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.
- 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 nvidiaAllen Brain Observatory dataset
If you have any questions about this project, please feel free to contact us. Email address: [wuu_yukun@163.com](