Computer-based tracking of single sperm
TL;DR: A robust single sperm tracking algorithm (SSTA) that can be used in laser optical trapping and sperm motility studies and is validated through examples and comparisons to commercially available computer-aided sperm tracking systems.
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Abstract: This paper describes a robust single sperm tracking algorithm (SSTA) that can be used in laser optical trapping and sperm motility studies. The algorithm creates a region of interest (ROI) centered about a sperm selected by the user. SSTA contrast enhances the ROI image and implements a modified four-class thresholding method to extract the tracked sperm as it transitions in and out of focus. The nearest neighbor method is complemented with a speed-check feature to aid tracking in the presence of additional sperm or other particles. SSTA has a collision-detection feature for real or perceived collision or near-miss cases between two sperm. Subsequent postcollision analysis employs three criteria to distinguish the tracked sperm in the image. The efficacy of SSTA is validated through examples and comparisons to commercially available computer-aided sperm tracking systems.
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Fig. 5 Post collision analysis. a Illustration of a collision in which the tracked sperm starting at point A1 and a second starting at point B2 swim toward a common point A2=B2, collide, and swim in new trajectories toward A3 and B3—it is unknown which sperm is the tracked one postcollision; b vector diagram measures displacements L and swimming angles between the precollision track of A and the two potential postcollision end points A3 and B3; c first frame in which a sperm B enters the ROI of tracked sperm A raw phase contrast image ; d enhanced image of c ; e first frame in which sperm A and sperm B merge contrast enhanced ; f first frame of separation is detected by collision detection and both sperm are tracked for 20 frames; g since sperm B in f is closest to the merged sperm in e , the nearest neighbor method mistakenly identifies it as sperm A for the next 20 frames notice the ROI is about sperm B ; h postcollision analysis finds sperm A is 1000 times as likely as sperm B to be the correct sperm and the ROI is transferred to sperm A. Frame numbers: c d =29, e =34, f =40, g =59, h =60. 
Fig. 6 a Distribution of RVi,j labeled Rv ; b distribution of RDi,j labeled RD . The distributions were first normalized such that integration under the curve yields unity and then scaled by their maximum frequency value in order to compare to Eqs. 9 and 10 . are the normalized values, — the best Gaussian fit and --- Eq. 9 and 10 for a or b , respectively, with = 10. Fit parameters for a are mean=1.0183 1.0162, 1.003 and standard deviation=0.1401 0.1387, 0.1416 ; fit parameters for b are mean=1.0110 1.0087, 1.0134 and standard deviation=0.1593 0.1576, 0.1609 reported as value 95% confidence interval . 
Fig. 1 a Raw phase contrast image of dog sperm acquired with a 40 oil immersion objective lens and a 0.33 demagnifier. Notice the contrast and brightness differences between the four sperm and the debris in the field, which are all at different focal positions relative to the high NA objective’s focal plane. b Contrast enhancement produces a black background with bright sperm and debris. The square box indicates the ROI of the tracked sperm. 
Fig. 2 a Raw phase contrast image containing two axially separated sperm; b after contrast enhancement; c binary mask demonstrates that two-class segmentation maps the dim sperm to the background; d binary mask demonstrates that SSTA’s segmentation method finds both sperm. Gray pixels in the two white regions represent the calculated centroids; e for illustration, two phantom particles particle A with low brightness and particle B with high brightness represent the two sperm; f mask of the first brightest class contains the central portion of particle B; g the second class contains a portion of particle B; h the third class contains particle A and the dimmest pixels of particle B; i The final mask identifies pixels from both phantoms. 
Table 1 Calculated parameters for the postcollision analysis of the video sequence represented in Fig. 5. The cost function C was orders of magnitude higher for sperm A, the correct sperm to track postcollision. 
Table 2 Comparison of key features used by SSTA and the HTMIVOS system for sperm tracking.
Citations
Mitochondrial membrane potential.
Ljubava D. Zorova,Vasily A. Popkov,Egor Y. Plotnikov,Denis N. Silachev,Irina B. Pevzner,S. S. Jankauskas,V. A. Babenko,S. D. Zorov,Anastasia V. Balakireva,Magdalena Juhaszova,Steven J. Sollott,Dmitry B. Zorov,Dmitry B. Zorov +12 more
TL;DR: Additional potential mechanisms for which ΔΨm is essential for maintenance of cellular health and viability are proposed and recommendations how to accurately measure ΔΩm in a cell are provided and potential sources of artifacts are discussed.
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Lensless imaging for simultaneous microfluidic sperm monitoring and sorting
Xiaohui Zhang,Imran Khimji,Umut A. Gurkan,Hooman Safaee,Paolo N. Catalano,Hasan Onur Keles,Emre Kayaalp,Utkan Demirci,Utkan Demirci +8 more
TL;DR: The integrated system enables the sorting and tracking of a population of sperm that have been placed in a microfluidic channel and can be monitored in both horizontal and vertical configuration similar to a swim-up column method used clinically.
Automatic Tracking and Motility Analysis of Human Sperm in Time-Lapse Images
TL;DR: An image processing method, based on radar tracking algorithms, that detects and tracks automatically the swimming paths of human sperm cells in timelapse microscopy image sequences of the kind that is analyzed by fertility clinics and provides medical practitioners and researchers with more useful data than are currently available.
97
Validation of a novel computer-assisted sperm analysis (CASA) system using multitarget-tracking algorithms.
Mathew Tomlinson,Karen Pooley,Tracey Simpson,Thomas Newton,James Hopkisson,Kannamanadias Jayaprakasan,Rajisha Jayaprakasan,Asad Naeem,Tony P. Pridmore +8 more
TL;DR: The novel CASA system was able to provide semen quality measurements for sperm concentration and motility measurements which were at least as reliable as current manual methods.
89
Uncertainty of measurement and clinical value of semen analysis: has standardisation through professional guidelines helped or hindered progress?
TL;DR: It is suggested that diagnostic semen analysis has no more clinical value today than it had 25–30 years ago, and both the confusion surrounding its evidence base and the low level of confidence in the clinical setting is attributable to an associated high level of ‘uncertainty’.
58
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Micromanipulation of sperm by a laser generated optical trap - eScholarship
Y Tadir,WH Wright,Omid Vafa,T Ord,RH Asch,MW Berns +5 more
- 01 Jan 1989
TL;DR: In this paper, a Nd:YAG laser beam was coupled to a conventional microscope and focused into the viewing plane by the objective lens, and sperm were caught in the trap and manipulated by a joy stick controlled motorized stage.
•Journal Article
Determination of motility forces of human spermatozoa using an 800 nm optical trap
Karsten König,Lars O. Svaasand,Yagang Liu,G.J. Sonek,Pasquale Patrizio,Y Tadir,Michael W. Berns,Bruce J. Tromberg +7 more
TL;DR: The measurement and calculation of trapping forces on ellipsoidal specimens, and the determination of intrinsic motility forces of human spermatozoa by employing an 800 nm optical trap ("laser tweezers"), are presented.
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