BACKGROUND Total motile sperm count (TMSC) and curvilinear velocity (VCL) are two important parameters in preliminary semen analysis for male infertility. Traditionally, both parameters are evaluated manually by embryologists or automatically using an expensive computer-assisted sperm analysis (CASA) instrument. The latter applies a point-tracking method using an image processing technique to detect, recognize and classify each of the target objects, individually, which is complicated. However, as semen is dense, manual counting is exhausting while CASA suffers from severe overlapping and heavy computation. METHODS We proposed a simple frame-differencing method that tracks motile sperms collectively and treats their overlapping with a statistical occupation probability without heavy computation. The proposed method leads to an overall image of all of the differential footprint trajectories (DFTs) of all motile sperms and thus the overall area of the DFTs in a real-time manner. Accordingly, a theoretical DFT model was also developed to formulate the overall DFT area of a group of moving beads as a function of time as well as the total number and average speed of the beads. Then, using the least square fitting method, we obtained the optimal values of the TMSC and the average VCL that yielded the best fit for the theoretical DFT area to the measured DFT area. RESULTS The proposed method was used to evaluate the TMSC and the VCL of 20 semen samples. The maximum TMSC evaluated using the method is more than 980 sperms per video frame. The Pearson correlation coefficient (PCC) between the two series of TMSC obtained using the method and the CASA instrument is 0.946. The PCC between the two series of VCL obtained using the method and CASA is 0.771. As a consequence, the proposed method is as accurate as the CASA method in TMSC and VCL evaluations. CONCLUSION In comparison with the individual point-tracking techniques, the collective DFT tracking method is relatively simple in computation without complicated image processing. Therefore, incorporating the proposed method into a cell phone equipped with a microscopic lens can facilitate the design of a simple sperm analyzer for clinical or household use without advance dilution.

中文翻译：

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初步追踪精子的集体追踪方法。

背景技术总的精子活动量（TMSC）和曲线速度（VCL）是男性不育症初步精液分析中的两个重要参数。传统上，这两个参数由胚胎学家手动评估，或使用昂贵的计算机辅助精子分析（CASA）仪器自动评估。后者应用使用图像处理技术的点跟踪方法来分别检测，识别和分类每个目标对象，这很复杂。但是，由于精液很稠密，因此手工计数变得很累，而CASA却出现了严重的重叠和繁重的计算。方法我们提出了一种简单的区分帧的方法，该方法可以集体跟踪运动性精子，并以统计占用概率处理它们的重叠，而无需进行大量计算。所提出的方法产生了所有运动精子的所有差异足迹轨迹（DFT）的整体图像，并因此以实时方式产生了DFT的整体面积。因此，还开发了理论DFT模型，以将一组运动珠粒的总DFT面积公式化为时间以及珠粒的总数和平均速度的函数。然后，使用最小二乘拟合法，我们获得了TMSC的最佳值和平均VCL，这些值对理论DFT面积与所测DFT面积产生了最佳拟合。结果所提出的方法用于评估20个精液样品的TMSC和VCL。使用该方法评估的最大TMSC超过每个视频帧980个精子。使用该方法获得的两个TMSC系列与CASA仪器之间的Pearson相关系数（PCC）为0.946。使用该方法和CASA获得的两个VCL系列之间的PCC为0.771。结果，在TMSC和VCL评估中，所提出的方法与CASA方法一样准确。结论与单独的点跟踪技术相比，集体DFT跟踪方法在计算上相对简单，而无需复杂的图像处理。因此，将所提出的方法结合到配备有显微镜透镜的手机中可以方便设计用于临床或家庭使用的简单精子分析仪，而无需事先稀释。在TMSC和VCL评估中，所提方法与CASA方法一样准确。结论与单独的点跟踪技术相比，集体DFT跟踪方法在计算上相对简单，而无需复杂的图像处理。因此，将所提出的方法结合到配备有显微镜透镜的手机中可以方便设计用于临床或家庭使用的简单精子分析仪，而无需事先稀释。在TMSC和VCL评估中，所提方法与CASA方法一样准确。结论与单独的点跟踪技术相比，集体DFT跟踪方法在计算上相对简单，而无需复杂的图像处理。因此，将所提出的方法结合到配备有显微镜透镜的手机中可以方便设计用于临床或家庭使用的简单精子分析仪，而无需事先稀释。