Department of Control Systems, Kaunas University of Technology, 51423 Kaunas, Lithuania.
Department of Multimedia Engineering, Kaunas University of Technology, 51423 Kaunas, Lithuania.
Sensors (Basel). 2020 Dec 24;21(1):72. doi: 10.3390/s21010072.
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.
我们提出了一种基于区域卷积神经网络（R-CNN）架构的深度学习方法，用于评估人类精液视频中的精子头运动。该神经网络执行精子头的分割，而我们提出的中心坐标跟踪算法允许我们计算精子头的运动速度。我们在 VISEM（人类精子的多模态视频数据集）精子样本视频数据集上实现了 91.77%（95%置信区间，91.11-92.43%）的精子头检测准确率。精子头活力预测的平均绝对误差（MAE）为 2.92（95%置信区间，2.46-3.37），而实际和预测的精子头活力之间的皮尔逊相关系数为 0.969。下面呈现的实验结果将表明所提出的方法在自动化人工授精工作流程中的适用性。
