Shape mode analysis exposes movement patterns in biology: flagella and flatworms as case studies

We illustrate shape mode analysis as a simple, yet powerful technique to concisely describe complex biological shapes and their dynamics. We characterize undulatory bending waves of beating flagella and reconstruct a limit cycle of flagellar oscillations, paying particular attention to the periodicity of angular data. As a second example, we analyze non-convex boundary outlines of gliding flatworms, which allows us to expose stereotypic body postures that can be related to two different locomotion mechanisms. Further, shape mode analysis based on principal component analysis allows to discriminate different flatworm species, despite large motion-associated shape variability. Thus, complex shape dynamics is characterized by a small number of shape scores that change in time. We present this method using descriptive examples, explaining abstract mathematics in a graphic way.Comment: 20 pages, 6 figures, accepted for publication in PLoS On

Improving the performance and evaluation of computer-assisted semen analysis

Semen analysis is performed routinely in fertility clinics to analyze the quality of semen and sperm cells of male patients. The analysis is typically performed by trained technicians or by Computer-Assisted Semen Analysis (CASA) systems. Manual semen analysis performed by technicians is subjective, time-consuming, and laborious, and yet most fertility clinics perform semen analysis in this manner. CASA systems, which are designed to perform the same tasks automatically, have a considerable market share, yet many studies still express concerns about their accuracy and consistency. In this dissertation, the focus is on detection, tracking, and classification of sperm cells in semen images, key elements of CASA systems. The objective is to improve existing CASA algorithms and systems by applying validated computer vision, tracking, and computational intelligence algorithms. The first step of the study is the development of simulation models for generating synthetic images of semen samples. The images enable the assessment of CASA systems and their algorithms. Specifically, the simulation models generate time-lapse images of semen samples for various sperm image categories and include ground truth labels. The models exploit standard image processing operations such as point spread functions and 2D convolutions, as well as new models of sperm cell swimming, developed for this study. They embody multiple studies of sperm motility in the form of parameterized motion equations. Use cases are presented to use the swimming models and the simulated images to assess and compare algorithms for sperm cell segmentation, localization, and tracking. Second, a digital washing algorithm is presented for unwashed semen samples. Digital washing has the potential to replace the chemical washing techniques used by fertility clinics at present, which are costly, time-consuming, and unfriendly to the environment. The digital washing algorithm extracts features from moving sperm cells in an image, and uses these features to identify all sperm cells (moving and stationary) within each studied image (simulated or real). The effectiveness of the digital washing algorithm is demonstrated by comparing the performance of the proposed algorithm to other cell segmentation and detection techniques. Third, a classification algorithm for sperm cells is developed, based on their swimming patterns. The classification algorithm uses K-means clustering on a subset of motility parameters of sperm cells selected by the Artificial Bee Colony (ABC) algorithm. Results of classification and clustering are shown, using simulated and real semen images. Swimming pattern classification has the potential to increase understanding of the relationship between the distribution of sperm cell swimming modes in a patient’s semen image and the fertility of that patient. Lastly, a new method is presented to calculate motility parameters from sperm tracks. The movement of sperm cell is modeled as a sinusoidal traveling wave (“traveling sinusoid”). The amplitude and average path of a moving cell are estimated using an extended Kalman filter (EKF). The states estimated by the EKF include position, velocity, amplitude, and frequency of the traveling wave. The motility parameters calculated from this approach are shown to be superior to those calculated by other existing methods in terms of their accuracy and consistency. CASA developers will find in this study (and in the software made available) new tools to improve the performance of their designs, and to compare and contrast different proposed approaches and algorithms

A Bag of Features Based Approach for Classification of Motile Sperm Cells

The analysis of sperm morphology remains an essential process for diagnosis and treatment of male infertility. In this paper, a novel framework based on image processing is proposed to classify sperm cell images affected by noise due to their movement. This represents a challenge, articularly because the cells are not fixed or stained. The proposed framework is based on Speeded-Up Robust Features (SURF) combined with Bag of Features (BoF) models to quantise features computed by SURF. Support Vector Machines (SVMs) are used to classify the simplified feature vectors, extracted from sperm cell images, into normal, abnormal and noncell categories. The performance of this framework is compared to a similar model where the Histogram of Oriented Gradients (HOG) is used to extract features and SVMs is applied for their classification. The proposed framework allows to achieve classification results with an average accuracy of 90% with the SURF approach compared to 78% with the HOG approach

A Review on Automatic Analysis of Human Embryo Microscope Images

Over the last 30 years the process of in vitro fertilisation (IVF) has evolved considerably, yet the efficiency of this treatment remains relatively poor. The principal challenge faced by doctors and embryologists is the identification of the embryo with the greatest potential for producing a child. Current methods of embryo viability assessment provide only a rough guide to potential. In order to improve the odds of a successful pregnancy it is typical to transfer more than one embryo to the uterus. However, this often results in multiple pregnancies (twins, triplets, etc), which are associated with significantly elevated risks of serious complications. If embryo viability could be assessed more accurately, it would be possible to transfer fewer embryos without negatively impacting IVF pregnancy rates. In order to assist with the identification of viable embryos, several scoring systems based on morphological criteria have been developed. However, these mostly rely on a subjective visual analysis. Automated assessment of morphological features offers the possibility of more accurate quantification of key embryo characteristics and elimination of inter- and intra-observer variation. In this paper, we describe the main embryo scoring systems currently in use and review related works on embryo image analysis that could lead to an automatic and precise grading of embryo quality. We summarise achievements, discuss challenges ahead, and point to some possible future directions in this research field

Mixture gaussian V2 based microscopic movement detection of human spermatozoa

Healthy and superior sperm is the main requirement for a woman to get pregnant. To find out how the quality of sperm is needed several checks. One of them is a sperm analysis test to see the movement of sperm objects, the analysis is observed using a microscope and calculated manually. The first step in analyzing the scheme is detecting and separating sperm objects. This research is detecting and calculating sperm movements in video data. To detect moving sperm, the background processing of sperm video data is essential for the success of the next process. This research aims to apply and compare some background subtraction algorithms to detect and count moving sperm in microscopic videos of sperm fluid, so we get a background subtraction algorithm that is suitable for the case of sperm detection and sperm count. The research methodology begins with the acquisition of sperm video data. Then, preprocessing using a Gaussian filter, background subtraction, morphological operations that produce foreground masks, and compared with moving sperm ground truth images for validation of the detection results of each background subtraction algorithm. It also shows that the system has been able to detect and count moving sperm. The test results show that the MoG (Mixture of Gaussian) V2 (2 Dimension Variable) algorithm has an f-measure value of 0.9449 and has succeeded in extracting sperm shape close to its original form and is superior compared to other methods. To conclude, the sperm analysis process can be done automatically and efficiently in terms of time

The validity and reliability of computer-aided semen analyzers in performing semen analysis: a systematic review

Background: Computer-aided sperm analyzers (CASA) are currently used worldwide for semen analysis. However, there are doubts about their reliability to fully substitute the human operator. Therefore, this study aimed to systematically review the current literature comparing results from semen evaluation by both CASA-based and manual approaches. Methods: A systematic screening of the literature was performed based on the PRISMA guidelines and by searching on PubMed, Scopus, and Embase databases. Results: A total of 14 studies were included. Our results showed a high degree of correlation for sperm concentration and motility when analysis was performed either manually or by using a CASA system. However, CASA results showed increased variability in low (60 million/mL) concentration specimens, while sperm motility assessment was inaccurate in samples with higher concentration or in the presence of non-sperm cells and debris. Morphology results showed the highest level of difference, due to the high amount of heterogeneity seen between the shapes of the spermatozoa either in one sample or across multiple samples from the same subject. Conclusions: Overall, our study suggests CASA systems as a valid alternative for the evaluation of semen parameters in clinical practice, especially for sperm concentration and motility. However, further technological improvements are required before these devices can one day completely replace the human operator. Artificial intelligence-based CASA devices promise to offer higher efficiency of the analysis and improve the reliability of results. Keywords: Computer-aided sperm analyzers (CASA); computer-assisted sperm analysis; semen analysis; sperm concentratio