Earticle

초록

영어

Background: The morphological characteristics of sperm are essential for assessing sperm quality, which partially influences reproductive efficiency variables such as litter size in sows. Recently, deep-learning-based object detection algorithms have been explored to detect and classify sperm morphological features, with the training of these models requiring sperm microscopy image data. The performance of these models in detecting morphological features was significantly affected by the size of the dataset and the image quality of the images. Methods: This study proposed a deep-learning-based super-resolution (SR) algorithm to enhance the quality of sperm microscope images. The model was trained using a dataset consisting of high-resolution (HR) original and low-resolution (LR) images generated by downscaling the original images through bicubic interpolation. The SR results of the test dataset were compared with those of the original HR images to evaluate their performances using the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Statistical analyses were conducted to examine the performance differences based on the training models and dataset types. Results: This study indicated that the SR images showed no significant differences in PSNR (p = 0.9740) and SSIM (p = 0.9864) compared with the original HR images in same magnification. Moreover, increased processing speed was observed with reductions in model hyperparameters. While processing SR images improved spatial resolution across various microscopic magnifications, the overall image quality did not exceed that of the original HR images. Conclusions: SR models applied to sperm microscopy images outperformed conventional SR algorithms. These findings suggest that SR algorithms hold promise for improving the quality of LR microscopic images in future deep-learning-based object-detection algorithms.

목차

ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
Sperm preparation
Data preprocessing
Model selection and hyper-parameter
Evaluation metrics
Statistical analysis
RESULTS
SR of sperm microscopic images at the same magnification
Super-resolution of sperm microscopic images at the different magnification
DISCUSSION
CONCLUSION
REFERENCES

키워드

저자

• Jaeuk Cho [ College of Animal Life Sciences, Kangwon National University, Chuncheon 24341, Korea ]
• Eunju Seok [ College of Animal Life Sciences, Kangwon National University, Chuncheon 24341, Korea ]
• Sang-Hee Lee [ College of Animal Life Sciences, Kangwon National University/School of ICT, University of Tasmania, Hobart 7005, Australia ] Corresponding Author

참고문헌

발행기관

간행물

관심저널 등록

• 간행물명

  Journal of Animal Reproduction and Biotechnology

• 간기

  계간

• pISSN

  2671-4639

• eISSN

  2671-4663

• 수록기간

  2019~2026

• 십진분류

  KDC 527 DDC 636