MS-Con-EM-Seg

This is an official implement for Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation (ICASSP 24) Paper

Environment Setup

To set up the required environment, you can choose from the following options:

Using pip: You can install the necessary Python dependencies from the requirements.txt file using the following command:
```
pip install -r requirements.txt
```

We highly recommend using Docker to set up the required environment. Two Docker images are available for your convenience:

Using Docker from ali cloud:
- registry.cn-hangzhou.aliyuncs.com/cyd_dl/monai-vit:v26
```
docker pull registry.cn-hangzhou.aliyuncs.com/cyd_dl/monai-vit:v26
```
Using docker from dockerhub:
- cyd_docker:v1
```
docker pull ydchen0806/cyd_docker:v1
```

Dataset Download

The datasets required for pre-training and segmentation are as follows:

Dataset Type	Dataset Name	Description	URL
Pre-training Dataset	Region of FAFB Dataset	Fly brain dataset for pre-training	EM Pretrain Dataset
Segmentation Dataset	CREMI Dataset	Challenge on circuit reconstruction datasets	CREMI Dataset
Segmentation Dataset	AC3/AC4	AC3/AC4 Dataset	Mouse Brain GoogleDrive

Usage Guide

1. Pretraining

python pretrain.py -c pretraining_all -m train

2. Finetuning

python finetune.py -c seg_3d -m train -w [your pretrained path]

Cite

If you find this code or dataset useful in your research, please consider citing our paper:

@inproceedings{chen2024learning,
  title={Learning multiscale consistency for self-supervised electron microscopy instance segmentation},
  author={Chen, Yinda and Huang, Wei and Liu, Xiaoyu and Deng, Shiyu and Chen, Qi and Xiong, Zhiwei},
  booktitle={ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={1566--1570},
  year={2024},
  organization={IEEE}
}

Contact

If you need any help or are looking for cooperation feel free to contact us. cyd0806@mail.ustc.edu.cn

ydchen0806/MS-Con-EM-Seg