A method for sperm activity analysis based on feature point detection network in deep learning

Sperm motility is an important index to evaluate semen quality. Computer-assisted sperm analysis (CASA) is based on the sperm image, through the image-processing algorithm to detect the position of the sperm target and track tracking, so as to judge the sperm activity. Because of the small and dense sperm targets in sperm images, traditional image-processing algorithms take a long time to detect sperm targets, while target-detection algorithms based on the deep learning have a lot of missed detection problems in the process of sperm target detection. In order to accurately and efficiently analyze sperm activity in the sperm image sequence, this article proposes a sperm activity analysis method based on the deep learning. First, the sperm position is detected through the deep learning feature point detection network based on the improved SuperPoint, then the multi-sperm target tracking is carried out through SORT and the sperm motion trajectory is drawn, and at last the sperm survival is judged through the sperm trajectory to realize the analysis of sperm activity. The experimental results show that this method can effectively analyze the sperm activity in the sperm image sequence. At the same time, the average detection speed of the sperm target detection method in the detection process is 65fps, and the detection accuracy is 92%.