Abstract: Sperm head shape is an important indicator in the analysis of sperm morphology, which is very important for diagnosing male infertility. Therefore, it is very important to segment the sperm head accurately and efficiently. Based on this, this paper builds a new encoder-decoder segmentation network that combines stacked residual block and residual hybrid dilated convolution. Firstly, we build a dataset for segmenting the head of human sperm, which contains 1207 images, and then use it to train and test our network. The proposed network is able to achieve excellent segmentation results in low-quality images that are unstained and contain multiple sperms, and obtain a Dice coefficient of 96.06% on the validation set. The experimental results show that the stacked residual module and the residual hybrid dilated convolution module significantly improve the segmentation performance. In addition, the proposed network processes the images that show the original true state of the sperm, and the accurate segmentation results are very helpful for the doctor’s clinical diagnosis.

Key words: human sperm head segmentation, sperm deformity, deep learning, residual block, hybrid dilated convolution