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

Advancement of methods for passive acoustic monitoring : a framework for the study of deep-diving cetacean

Marine mammals face numerous anthropogenic threats, including fisheries interactions, ocean noise, ship strikes, and marine debris. Monitoring the negative impact on marine mammals through the assessment of population trends requires information about population size, spatiotemporal distribution, population structure, and animal behavior. Passive acoustic monitoring has become a viable method for gathering long-term data on highly mobile and notoriously cryptic marine mammals. However, passive acoustic monitoring still faces major challenges requiring further development of robust analysis tools, especially as it becomes increasingly used in applied conservation for long-term and large-scale studies of endangered or data deficient species such as sperm or beaked whales. Further challenges lie in the translation of animal presence into quantitative population density estimates since methods must control for variation in acoustic detectability of the target species, environmental factors, and for species-specific vocalization rates. The main contribution of this thesis is the advancement of the framework for long-term quantitative monitoring of cetacean species, applied to deep-divers like sperm and beaked whales. Fully-automated methods were developed and implemented to different populations of beaked whales in different conditions. This provided insight into generalization capabilities of these automatic techniques and best practices. However, implementing these tool kits is not always practical, and alternative methods for additional data processing were developed to expeditiously serve multiple purposes including annotation of individual sounds, evaluation of data in order to provide a highly dynamic technique, and classification for quantitative monitoring studies. This work also presents the longest time series of sperm whale presence using passive acoustic monitoring for over seven years in the Gulf of Mexico. Echolocation clicks were detected and discriminated from other sounds to understand the spatiotemporal distribution and structure of the population. A series of steps were implemented to provide adequate parameters and characteristics of the target population for density estimation using an echolocation click-based method. This allowed for the study of the Gulf of Mexico’s sperm whale population, providing significant progress towards the understanding of the population structure, distribution, and trends, in addition to potential long-term impacts of the well-known catastrophic Deepwater Horizon oil spill and other anthropogenic activities. The emergence of innovative approaches for detecting the presence of marine mammals and documenting human interactions can provide insight into ecosystem change. These species can be used as sentinels of ocean health to ensure the conservation of their marine environment into the next epoch.Els mamífers marins s'enfronten a nombroses amenaces antropogèniques, incloses les interaccions pesqueres, la contaminació acústica als oceans, les coalicions amb vaixells i els residus marins. El seguiment de l'impacte d’aquestes amenaces en els mamífers marins mitjançant l'avaluació de les tendències poblacionals requereix informació sobre la mida i l’estructura poblacional, la distribució espaciotemporal i el comportament dels animals. El seguiment amb sistemes d’acústica passiva s'ha convertit en un mètode viable per recollir dades a llarg termini de mamífers marins altament mòbils i críptics. Tanmateix, el seguiment acústic passiu encara ha d’afrontar reptes importants en el desenvolupament d'eines d'anàlisi robustes, especialment de cara al recent increment en el seu ús en la conservació aplicada a seguiments a llarg termini i a gran escala d'espècies en perill d'extinció o amb dades insuficients com ara el catxalot o els zífids. Altres reptes són traduir la presència d’animals a estimacions quantitatives de densitat poblacional, degut a que els mètodes han de controlar la variabilitat en la detecció acústica de les espècies en qüestió, els factors ambientals i les freqüències de vocalització específiques de cada espècie. La principal contribució d'aquesta tesi és l'avanç en els mètodes de seguiment quantitatiu a llarg termini de les espècies de cetacis, aplicat a espècies que viuen a grans profunditats com el catxalot i els zífids. Durant aquesta tesi, s’han desenvolupat i aplicat mètodes totalment automatitzats per detectar zífids de diferents poblacions i en diferents condicions. Aquests mètodes han proporcionat informació sobre la capacitat de generalització d'aquestes tècniques automàtiques i han permès fer recomanacions de bones pràctiques. Tanmateix, degut a que la implementació d’aquestes eines no és sempre pràctic, s’han desenvolupat mètodes per al processament de dades de forma expeditiva, que tenen diversos propòsits, que inclouen l’anotació de sons individuals, l’avaluació de dades per proporcionar una tècnica més dinàmica i la classificació per a estudis de seguiment quantitatiu. Aquest treball també presenta la sèrie temporal més llarga documentada de la presència de catxalots obtinguda mitjançant tècniques de seguiment acústic passiu durant més de set anys al Golf de Mèxic. S’han detectat i discriminat les senyals d'ecolocalització d'altres sons per tal de comprendre la distribució i l'estructura espaciotemporal d’aquesta població de catxalots. S’han implementat una sèrie de passos per proporcionar paràmetres i característiques de la població amb l’objectiu d'estimar la densitat mitjançant un mètode basat en senyals d’ecolocalització. Aquesta implementació ha permès l'estudi de la població de catxalots del Golf de Mèxic i ha suposat un progrés significatiu per la comprensió de l'estructura, la distribució i les tendències poblacionals, així com dels potencials impactes a llarg termini del catastròfic vessament de petroli de la plataforma Deepwater Horizon i altres activitats antropogèniques.Postprint (published version

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

Veni Vidi Vici, A Three-Phase Scenario For Parameter Space Analysis in Image Analysis and Visualization

Automatic analysis of the enormous sets of images is a critical task in life sciences. This faces many challenges such as: algorithms are highly parameterized, significant human input is intertwined, and lacking a standard meta-visualization approach. This paper proposes an alternative iterative approach for optimizing input parameters, saving time by minimizing the user involvement, and allowing for understanding the workflow of algorithms and discovering new ones. The main focus is on developing an interactive visualization technique that enables users to analyze the relationships between sampled input parameters and corresponding output. This technique is implemented as a prototype called Veni Vidi Vici, or "I came, I saw, I conquered." This strategy is inspired by the mathematical formulas of numbering computable functions and is developed atop ImageJ, a scientific image processing program. A case study is presented to investigate the proposed framework. Finally, the paper explores some potential future issues in the application of the proposed approach in parameter space analysis in visualization

Review of Cetacean's click detection algorithms

The detection of echolocation clicks is key in understanding the intricate behaviors of cetaceans and monitoring their populations. Cetacean species relying on clicks for navigation, foraging and even communications are sperm whales (Physeter macrocephalus) and a variety of dolphin groups. Echolocation clicks are wideband signals of short duration that are often emitted in sequences of varying inter-click-intervals. While datasets and models for clicks exist, the detection and classification of clicks present a significant challenge, mostly due to the diversity of clicks' structures, overlapping signals from simultaneously emitting animals, and the abundance of noise transients from, for example, snapping shrimps and shipping cavitation noise. This paper provides a survey of the many detection and classification methodologies of clicks, ranging from 2002 to 2023. We divide the surveyed techniques into categories by their methodology. Specifically, feature analysis (e.g., phase, ICI and duration), frequency content, energy based detection, supervised and unsupervised machine learning, template matching and adaptive detection approaches. Also surveyed are open access platforms for click detections, and databases openly available for testing. Details of the method applied for each paper are given along with advantages and limitations, and for each category we analyze the remaining challenges. The paper also includes a performance comparison for several schemes over a shared database. Finally, we provide tables summarizing the existing detection schemes in terms of challenges address, methods, detection and classification tools applied, features used and applications.Comment: 23 pages, 6 tables, 4 figure

motilitAI: a machine learning framework for automatic prediction of human sperm motility

In this article, human semen samples from the Visem dataset are automatically assessed with machine learning methods for their quality with respect to sperm motility. Several regression models are trained to automatically predict the percentage (0–100) of progressive, non-progressive, and immotile spermatozoa. The videos are adopted for unsupervised tracking and two different feature extraction methods—in particular custom movement statistics and displacement features. We train multiple neural networks and support vector regression models on the extracted features. Best results are achieved using a linear Support Vector Regressor with an aggregated and quantized representation of individual displacement features of each sperm cell. Compared to the best submission of the Medico Multimedia for Medicine challenge, which used the same dataset and splits, the mean absolute error (MAE) could be reduced from 8.83 to 7.31. We provide the source code for our experiments on GitHub (Code available at: https://github.com/EIHW/motilitAI)

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