Deep learning-based morphological analysis of human sperm.

Area of Science:

• Reproductive Biology
• Computer Vision
• Artificial Intelligence

Background:

• Sperm head morphology is a key indicator of male semen quality.
• Traditional 2D sperm morphology analysis has limitations in accurately representing overall quality.
• Existing methods struggle with the dynamic and multi-faceted nature of sperm characteristics.

Purpose of the Study:

• To develop a joint learning model for simultaneous sperm head segmentation and morphological category prediction.
• To overcome the limitations of static 2D imaging in assessing sperm morphology and quality.
• To create a more accurate and efficient method for semen analysis.

Main Methods:

• A deep-learning-based tracking and detection system was employed to dynamically acquire multi-frame and multi-angle sperm images.
• A multi-task model was developed to synthesize sperm morphology using predicted category and calculated ellipticity from segmented sperm heads.
• The system processes time-series images to determine sperm morphology.

Main Results:

• The proposed joint learning model outperforms traditional computer-assisted sperm assessment and 3D reconstruction methods.
• The approach enables end-to-end analysis of viable spermatozoa.
• The system requires minimal computing power and utilizes standard laboratory equipment.

Conclusions:

• The novel deep learning approach provides a more accurate and comprehensive assessment of sperm morphology and quality.
• This method enhances semen analysis by utilizing dynamic, multi-angle imaging.
• The system offers a practical and efficient solution for embryology laboratories.