Deep Learning Based Evaluation of Spermatozoid Motility for Artificial Insemination

Viktorija Valiuškaitė; Vidas Raudonis; Rytis Maskeliūnas; Robertas Damaševičius; Tomas Krilavičius

doi:10.3390/s21010072

Deep Learning Based Evaluation of Spermatozoid Motility for Artificial Insemination

Sensors (Basel). 2020 Dec 24;21(1):72. doi: 10.3390/s21010072.

Authors

Viktorija Valiuškaitė¹, Vidas Raudonis¹, Rytis Maskeliūnas², Robertas Damaševičius^{3

4}, Tomas Krilavičius³

Affiliations

¹ Department of Control Systems, Kaunas University of Technology, 51423 Kaunas, Lithuania.
² Department of Multimedia Engineering, Kaunas University of Technology, 51423 Kaunas, Lithuania.
³ Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania.
⁴ Faculty of Applied Mathematics, Silesian University of Technology, 444-100 Gliwice, Poland.

Abstract

We propose a deep learning method based on the Region Based Convolutional Neural Networks (R-CNN) architecture for the evaluation of sperm head motility in human semen videos. The neural network performs the segmentation of sperm heads, while the proposed central coordinate tracking algorithm allows us to calculate the movement speed of sperm heads. We have achieved 91.77% (95% CI, 91.11-92.43%) accuracy of sperm head detection on the VISEM (A Multimodal Video Dataset of Human Spermatozoa) sperm sample video dataset. The mean absolute error (MAE) of sperm head vitality prediction was 2.92 (95% CI, 2.46-3.37), while the Pearson correlation between actual and predicted sperm head vitality was 0.969. The results of the experiments presented below will show the applicability of the proposed method to be used in automated artificial insemination workflow.

Keywords: convolutional neural network (CNN); deep learning; sperm head detection; sperm quality.

MeSH terms

Deep Learning*
Humans
Insemination, Artificial*
Male
Neural Networks, Computer
Semen Analysis*
Spermatozoa