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Deep Learning Based Evaluation of Spermatozoid Motility for Artificial Insemination.

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.

SUBMITTER: Valiuskaite V

PROVIDER: S-EPMC7795243 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Deep Learning Based Evaluation of Spermatozoid Motility for Artificial Insemination.

Valiuškaitė Viktorija V Raudonis Vidas V Maskeliūnas Rytis R Damaševičius Robertas R Krilavičius Tomas T

Sensors (Basel, Switzerland) 20201224 1

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 ...[more]

PMID: 33374461

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