Human sperm morphology assessment since 2010: experience of an Australian external quality assurance programme

Matson, Phillip; Kitson, Michelle; Zuvela, Emily

doi:10.1016/j.rbmo.2021.11.005

Cited by 3 publications

(1 citation statement)

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“…A fundamental challenge in the diagnosis and treatment of infertility lies in the rapid and accurate analysis of semen samples provided by the laboratory. Sperm concentration, morphology [ 3 , 4 ] and DNA fragments are important indicators for semen analysis [ 5 ]. In addition, another important indicator affecting male fertility is sperm motility [ 6 , 7 , 8 , 9 ], and high-quality detection methods are the key to accurately assessing sperm motility.…”

Section: Introductionmentioning

confidence: 99%

YOLOv5s-SA: Light-Weighted and Improved YOLOv5s for Sperm Detection

et al. 2023

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Sperm detection performance is particularly critical for sperm motility tracking. However, there are a large number of non-sperm objects, sperm occlusion and poorly detailed texture features in semen images, which directly affect the accuracy of sperm detection. To solve the problem of false detection and missed detection in sperm detection, a multi-sperm target detection model, Yolov5s-SA, with an SA attention mechanism is proposed based on the YOLOv5s algorithm. Firstly, a depthwise, separable convolution structure is used to replace the partial convolution of the backbone network, which can ensure stable precision and reduce the number of model parameters. Secondly, a new multi-scale feature fusion module is designed to enhance the perception of feature information to supplement the positional information and high-resolution of the deep feature map. Finally, the SA attention mechanism is integrated into the neck network before the output of the feature map to enhance the correlation between the feature map channels and improve the fine-grained feature fusion ability of YOLOv5s. Experimental results show that compared with various YOLO algorithms, the proposed algorithm improves the detection accuracy and speed to a certain extent. Compared with the YOLOv3, YOLOv3-spp, YOLOv5s and YOLOv5m models, the average accuracy increases by 18.1%, 15.2%, 6.9% and 1.9%, respectively. It can effectively reduce the missed detection of occluded sperm and achieve lightweight and efficient multi-sperm target detection.

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