The Computer-Assisted Sperm Analysis (CASA) Technique for Sperm Morphology Evaluation

Wang, Yongxin; Jia, Yanping; Yuchi, Ming; Ding, Mingyue

doi:10.1109/icbmi.2011.21

Cited by 5 publications

(5 citation statements)

References 13 publications

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“…Computer-assisted sperm analysis CASA mainly evaluates the density, concentration, activity, and morphology of sperm (Wang et al, 2011), among which evaluation and analysis of sperm activity is a very important link. CASA can determine the sperm activity by detecting the target location of sperm in the sperm image and tracking the trajectory to determine the survival of each sperm in the image and then analyze the sperm activity.…”

Section: Related Workmentioning

confidence: 99%

“…Because the analysis process depends on the experience level of medical personnel, this process is highly subjective. The emergence of Computer-Assisted Sperm Analysis (CASA; Wang et al, 2011) greatly reduced the difficulty and subjectivity of Sperm Analysis. However, CASA's calculation of sperm parameters requires accurate and reliable internal algorithms.…”

Section: Introductionmentioning

confidence: 99%

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A method for sperm activity analysis based on feature point detection network in deep learning

Chen¹,

Yang²,

Chen³

et al. 2022

Front. Comput. Sci.

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Sperm motility is an important index to evaluate semen quality. Computer-assisted sperm analysis (CASA) is based on the sperm image, through the image-processing algorithm to detect the position of the sperm target and track tracking, so as to judge the sperm activity. Because of the small and dense sperm targets in sperm images, traditional image-processing algorithms take a long time to detect sperm targets, while target-detection algorithms based on the deep learning have a lot of missed detection problems in the process of sperm target detection. In order to accurately and efficiently analyze sperm activity in the sperm image sequence, this article proposes a sperm activity analysis method based on the deep learning. First, the sperm position is detected through the deep learning feature point detection network based on the improved SuperPoint, then the multi-sperm target tracking is carried out through SORT and the sperm motion trajectory is drawn, and at last the sperm survival is judged through the sperm trajectory to realize the analysis of sperm activity. The experimental results show that this method can effectively analyze the sperm activity in the sperm image sequence. At the same time, the average detection speed of the sperm target detection method in the detection process is 65fps, and the detection accuracy is 92%.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A method for sperm activity analysis based on feature point detection network in deep learning

Chen¹,

Yang²,

Chen³

et al. 2022

Front. Comput. Sci.

View full text Add to dashboard Cite

show abstract

“…Modern CASA systems "have been designed to objectively and quantitatively measure several aspects of sperm structure and function, aiming to provide high levels of intra-and inter-laboratory consistency" [1,2]. To achieve this aim, methods of noise filtering, image segmentation, localization, multi-object tracking, and machine learning were employed [3][4][5][6][7][8][9][10][11][12][13][14][15].…”

Section: Researchmentioning

confidence: 99%

“…We generate the cell head movement (ribbon-like movement along a linear path) using equations (12)(13)(14)(15). The position along the linear path is denoted (x c , y c ) and the position along the ribbon-like movement is denoted (P x (t), P y (t)).…”

Section: Swimming Modelsmentioning

confidence: 99%

“…The horizontal and vertical positions along the linear path (x c , y c ) change at the rate of horizontal and vertical velocity in the direction of forward movement θ r (equation ( 12)). The position along a "ribbon-like" path (P x (t), P y (t)) is generated using equations (13)(14). The ribbon is shown in figure 14b, where r v is the the height of the ribbon and r h is its width.…”

Section: Swimming Modelsmentioning

confidence: 99%

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An Assessment Tool for Computer-Assisted Semen Analysis (CASA) Algorithms

Choi

Alkhoury

Urbano

et al. 2022

Preprint

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Background: Computer-Assisted Semen Analysis (CASA) enables reliable analysis of semen images, and is designed to process large number of images with high consistency, accuracy, and repeatability. Design and testing of CASA algorithms can be accelerated greatly if reliable simulations of semen images under a variety of conditions and sample quality modes are available. Using lifelike simulation of semen images can quantify the performance of existing and proposed CASA algorithms, since the parameters of the simulated image are known and controllable. Methods: We present simulation models for sperm cell image and swimming modes observed in real 2D (top-down) images of sperm cells in laboratory specimen. The models simulate human sperm using four (4) types of swimming, namely linear mean, circular, hyperactive, and immotile (or dead). Results: The simulation models are used in studying algorithms for segmentation, localization, and tracking of sperm cells. Several segmentation and localization algorithms were tested under varying levels of noise, and then compared using precision, recall, and the optimal subpattern assignment (OSPA) metric. Images of real human semen sample were used to validate the segmentation and localization observations obtained from simulations. An example is given of sperm cell tracking on simulated semen images of cells using the different tracking algorithms (nearest neighbor (NN), global nearest neighbor (GNN), probabilistic data association filter (PDAF), and joint probabilistic data association filter (JPDAF)). Tracking performance was evaluated through multi-object tracking precision (MOTP) and multi-object tracking accuracy (MOTA). Conclusion: Simulation models enable objective assessments of semen image processing algorithms. We demonstrate the use of a new simulation tool to assess and compare segmentation, localization, and tracking methods. The simulation software allows testing along a large spectrum of parameter values that control the appearance and behavior of simulated semen images. Users can generate scenarios of different characteristics and assess the effectiveness of different CASA algorithms in these environments. The simulation was used to assess and compare algorithms for segmentation and tracking of sperm cells in semen images.

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Sperm Cells Segmentation in Micrographic Images Through Lambertian Reflectance Model

Medina-Rodríguez

Guzmán-Masías²,

Alatrista-Salas

et al. 2015

Computer Analysis of Images and Patterns

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The Computer-Assisted Sperm Analysis (CASA) Technique for Sperm Morphology Evaluation

Cited by 5 publications

References 13 publications

A method for sperm activity analysis based on feature point detection network in deep learning

A method for sperm activity analysis based on feature point detection network in deep learning

An Assessment Tool for Computer-Assisted Semen Analysis (CASA) Algorithms

Sperm Cells Segmentation in Micrographic Images Through Lambertian Reflectance Model

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