Automated Sperm Assessment Framework and Neural Network Specialized for Sperm Video Recognition
This work addresses infertility diagnosis by providing a more comprehensive automated tool for sperm assessment, though it is incremental as it builds on existing deep learning methods with a new dataset and model.
The authors tackled the problem of automated sperm assessment by constructing a video dataset with soft labels and proposing a neural network, RoSTFine, for sperm video recognition, which improved assessment performances compared to existing models and focused on important sperm parts like the head and neck.
Infertility is a global health problem, and an increasing number of couples are seeking medical assistance to achieve reproduction, at least half of which are caused by men. The success rate of assisted reproductive technologies depends on sperm assessment, in which experts determine whether sperm can be used for reproduction based on morphology and motility of sperm. Previous sperm assessment studies with deep learning have used datasets comprising images that include only sperm heads, which cannot consider motility and other morphologies of sperm. Furthermore, the labels of the dataset are one-hot, which provides insufficient support for experts, because assessment results are inconsistent between experts, and they have no absolute answer. Therefore, we constructed the video dataset for sperm assessment whose videos include sperm head as well as neck and tail, and its labels were annotated with soft-label. Furthermore, we proposed the sperm assessment framework and the neural network, RoSTFine, for sperm video recognition. Experimental results showed that RoSTFine could improve the sperm assessment performances compared to existing video recognition models and focus strongly on important sperm parts (i.e., head and neck).