A multi-task cascaded analysis network (MTCA-Net) for real-time tracking and segmentating sperm under high-resolution conditions.

This repository contains the implementation of the method described in our paper:

MTCA-Net: Multi-Task Cascade Analysis Network for Real-Time Sperm Quality Analysis

This project requires Python 3.9 and PyTorch 2.0.1. Follow these steps to set up the environment:

git clone https://github.com/Lijiajin0719/MTCA-Net.git
cd MTCA-Net

conda create -n MTCA-Net python=3.9
conda activate MTCA-Net

pip install -r requirements.txt

1.Download datasets from SHDet, SHSeg and SHSegHR

2.Extract files to the datasets directory

python train_detect.py

To see more intermediate results, check out ./runs/detect/AMF-YOLO....

python train_seg.py --batch_size 32 --epochs 300 --val_interval 5 --save_interval 50

To see more intermediate results, check out ./seg/run/Effusion_U2Net....

python test_detect.py

The test results will be saved to file here: ./runs/detect/test....

python test_seg.py --model_path seg/run/EffiFusion_U2Net_.../weights/best_model.pth --ap50_threshold 0.5

The test results will be saved to file here: ./seg/run/EffiFusion_U2Net_test....

python test_MTCA.py --detect_model_path runs/detect/AMF-YOLO/weights/best.pt --seg_model_path seg/run/EffiFusion_U2Net_test.../weights/best_model.pth --conf_threshold 0.6 --seg_threshold 0.5

The test results will be saved to file here: ./MTCA-Net/run/....

For any questions regarding the paper or this implementation, please feel free to contact the authors.

📩 Email: [email protected]

Our codebase is built with references to the following open-source projects:

• Ultralytics YOLO: The most popular real-time object detection model repository.

We sincerely appreciate the authors for open-sourcing their valuable work.

If you use this code for your research, please cite our paper.

@article{
  title={MTCA-Net: Multi-Task Cascade Analysis Network for Real-Time Sperm Quality Analysis},
  author={Li, Jiajin and Sun, Wenwen and He, Jun and Fan, Xinyu and Ge, Xuecheng and Lu, Fengya and Wang, Yadan and Wang, Yi and Zhang, Zhiguo and Wu, Qibing and Zhou, Jinhua},
  journal={Advanced Intelligent Systems},
  pages={e202501452},
  year={2026},
  doi={https://doi.org/10.1002/aisy.202501452},
  url={https://advanced.onlinelibrary.wiley.com/doi/abs/10.1002/aisy.202501452},
  publisher={Wiley Online Library}
}

🌟 We appreciate your interest in our work!