The accurate assessment of sperm morphology is crucial in andrological diagnostics, where the segmentation of sperm images presents significant challenges. Existing approaches frequently rely on large annotated datasets and often struggle with the segmentation of overlapping sperm and the presence of dye impurities. To address these challenges, this paper first analyzes the issue of overlapping sperm tails from a geometric perspective and introduces a novel clustering algorithm, Con2Dis, which effectively segments overlapping tails by considering three essential factors: CONnectivity, CONformity, and DIStance. Building on this foundation, we propose an unsupervised method, SpeHeatal, designed for the comprehensive segmentation of the SPErm HEAd and TAiL. SpeHeatal employs the Segment Anything Model(SAM) to generate masks for sperm heads while filtering out dye impurities, utilizes Con2Dis to segment tails, and then applies a tailored mask splicing technique to produce complete sperm masks. Experimental results underscore the superior performance of SpeHeatal, particularly in handling images with overlapping sperm.
Automated sperm morphology analysis plays a crucial role in the assessment of male fertility, yet its efficacy is often compromised by the challenges in accurately segmenting sperm images. Existing segmentation techniques, including the Segment Anything Model(SAM), are notably inadequate in addressing the complex issue of sperm overlap-a frequent occurrence in clinical samples. Our exploratory studies reveal that modifying image characteristics by removing sperm heads and easily segmentable areas, alongside enhancing the visibility of overlapping regions, markedly enhances SAM's efficiency in segmenting intricate sperm structures. Motivated by these findings, we present the Cascade SAM for Sperm Segmentation (CS3), an unsupervised approach specifically designed to tackle the issue of sperm overlap. This method employs a cascade application of SAM to segment sperm heads, simple tails, and complex tails in stages. Subsequently, these segmented masks are meticulously matched and joined to construct complete sperm masks. In collaboration with leading medical institutions, we have compiled a dataset comprising approximately 2,000 unlabeled sperm images to fine-tune our method, and secur
Infertility is a global health problem, and an increasing number of couples are seeking medical assistance to achieve reproduction, at least half of which are caused by men. The success rate of assisted reproductive technologies depends on sperm assessment, in which experts determine whether sperm can be used for reproduction based on morphology and motility of sperm. Previous sperm assessment studies with deep learning have used datasets comprising images that include only sperm heads, which cannot consider motility and other morphologies of sperm. Furthermore, the labels of the dataset are one-hot, which provides insufficient support for experts, because assessment results are inconsistent between experts, and they have no absolute answer. Therefore, we constructed the video dataset for sperm assessment whose videos include sperm head as well as neck and tail, and its labels were annotated with soft-label. Furthermore, we proposed the sperm assessment framework and the neural network, RoSTFine, for sperm video recognition. Experimental results showed that RoSTFine could improve the sperm assessment performances compared to existing video recognition models and focus strongly on i
The male fruit fly produces ~1.8 mm long sperm, thousands of which can be stored until mating in a ~200 micron sac, the seminal vesicle. While the evolutionary pressures driving such extreme sperm (flagellar) lengths have long been investigated, the physical consequences of their gigantism are unstudied. Through high-resolution three-dimensional reconstructions of in vivo sperm morphologies and rapid live imaging, we discovered that stored sperm are organized into a dense and highly aligned state. The packed flagella exhibit system-wide collective 'material' flows, with persistent and slow-moving topological defects; individual sperm, despite their extraordinary lengths, propagate rapidly through the flagellar material, moving in either direction along material director lines. To understand how these collective behaviors arise from the constituents' nonequilibrium dynamics, we conceptualize the motion of individual sperm as topologically confined to a reptation-like tube formed by its neighbors. Therein, sperm propagate through observed amplitude-constrained and internally driven flagellar bending waves, pushing off counter-propagating neighbors. From this conception, we derive a c
Male infertility is a major cause of couple infertility, often linked to abnormal sperm morphology. While deep learning models offer automated analysis, most lack interpretability, limiting their clinical adoption. This study proposes an attention-guided deep learning framework for sperm morphology classification. We combine a pretrained EfficientNet-B0 with a Convolutional Block Attention Module (CBAM) to focus on key areas of the sperm head, improving both accuracy and interpretability. Evaluated on the SMIDS and HuSHem public datasets, our model achieves accuracies of 90.2% and 93.9% (macro F1 scores of 0.913 and 0.948), outperforming SimpleCNN and standard EfficientNet-B0. Furthermore, we use Grad-CAM++ visualizations to highlight features influencing the model's decisions. The results demonstrate that this accurate and transparent framework is a practical tool for automated sperm analysis in fertility clinics.
In the low Reynolds number fluid environment that microswimmers encounter, back-and-forth motion cannot lead to net displacement. In mammalian sperm, the mechanical wave propagating along their single flagellum breaks the cancellation between back-and-forth motion and, therefore, is assumed to define the movement direction. Here, we show experimentally that the sperm head movement direction is not opposite to the wave propagation direction when sperm move in a viscoelastic fluid next to a solid substrate. The oscillation of the movement direction is out of phase with the oscillation of the wave direction, and the movement has a larger amplitude than the wave direction. When we tried to reconstruct the movement direction as a linear combination of the head orientation and the wave direction, we found that the contributions from these two changed in time. Further, the last bend of the flagellum does not move in the lab frame (as observed under the microscope) on a deformable polydimethylsiloxane (PDMS) substrate, which likely results from the flagellum indenting the surface, providing a new mechanism for locomotion. Overall, our results highlight the appearance of the sperm head in s
Sperm modulate their flagellar symmetry to navigate through complex physico-chemical environments and achieve reproductive function. Yet it remains elusive how sperm swim forwards despite the inherent asymmetry of several components that constitutes the flagellar engine. Despite the critical importance of symmetry, or the lack of it, on sperm navigation and its physiological state, there is no methodology to date that can robustly detect the symmetry state of the beat in free-swimming sperm in 3D.How does symmetric progressive swimming emerge even for asymmetric beating, and how can beating (a)symmetry be inferred experimentally? Here, we numerically resolve the fluid mechanics of swimming around asymmetrically beating spermatozoa. This reveals that sperm spinning critically regularizes swimming into persistently symmetric paths in 3D, allowing sperm to swim forwards despite any imperfections on the beat. The sperm orientation in three-dimensions, and not the swimming path, can inform the symmetry state of the beat, eliminating the need of tracking the flagellum in 3D. We report a surprising correspondence between the movement of sperm and spinning-top experiments, indicating that
Sperm DNA fragmentation (SDF) is a critical parameter in male fertility assessment that conventional semen analysis fails to evaluate. This study presents the validation of a novel artificial intelligence (AI) tool designed to detect SDF through digital analysis of phase contrast microscopy images, using the terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay as the gold standard reference. Utilising the established link between sperm morphology and DNA integrity, the present work proposes a morphology assisted ensemble AI model that combines image processing techniques with state-of-the-art transformer based machine learning models (GC-ViT) for the prediction of DNA fragmentation in sperm from phase contrast images. The ensemble model is benchmarked against a pure transformer `vision' model as well as a `morphology-only` model. Promising results show the proposed framework is able to achieve sensitivity of 60\% and specificity of 75\%. This non-destructive methodology represents a significant advancement in reproductive medicine by enabling real-time sperm selection based on DNA integrity for clinical diagnostic and therapeutic applications.
Active biological microswimmers typically combine persistent self-propulsion with cyclic motion generated by flagellar or ciliary beating. However, standard active Brownian motion (ABM) does not explicitly account for these intrinsic oscillations. This limitation is particularly relevant for sperm cells, whose transport depends not only on directional persistence and stochastic reorientation but also on periodic head motion. Here we introduce oscillatory active Brownian motion (OABM), a minimal extension of ABM in which the swimmer orientation undergoes directional diffusion while being modulated by a periodic angular drive. The model yields analytical expressions for experimentally relevant observables, including the time-averaged mean-squared displacement, velocity autocorrelation function, and transverse excursion amplitude. A central prediction is a crossover between two ballistic regimes: short-time motion governed by the swimming velocity and an intermediate regime with a reduced, oscillation-averaged effective velocity determined by the angular beat amplitude. Using trajectories of human sperm, we infer model parameters from standard motility measures. The model quantitative
Traditional sperm morphology analysis is based on tedious manual annotation. Automated morphology analysis of a high number of sperm requires accurate segmentation of each sperm part and quantitative morphology evaluation. State-of-the-art instance-aware part segmentation networks follow a "detect-then-segment" paradigm. However, due to sperm's slim shape, their segmentation suffers from large context loss and feature distortion due to bounding box cropping and resizing during ROI Align. Moreover, morphology measurement of sperm tail is demanding because of the long and curved shape and its uneven width. This paper presents automated techniques to measure sperm morphology parameters automatically and quantitatively. A novel attention-based instance-aware part segmentation network is designed to reconstruct lost contexts outside bounding boxes and to fix distorted features, by refining preliminary segmented masks through merging features extracted by feature pyramid network. An automated centerline-based tail morphology measurement method is also proposed, in which an outlier filtering method and endpoint detection algorithm are designed to accurately reconstruct tail endpoints. Exp
Assessment of sperm morphological quality remains a critical yet subjective component of male fertility evaluation, often limited by inter-observer variability and resource constraints. This study presents a comparative biomedical artificial intelligence framework evaluating an image-based deep learning model (HuSHeM) alongside a clinically grounded baseline derived from World Health Organization criteria augmented with the Systemic Inflammation Response Index (WHO(+SIRI)). The HuSHeM model was trained on high-resolution sperm morphology images and evaluated using an independent clinical cohort. Model performance was assessed using discrimination, calibration, and clinical utility analyses. The HuSHeM model demonstrated higher discriminative performance, as reflected by an increased area under the receiver operating characteristic curve with relatively narrow confidence intervals compared to WHO(+SIRI). Precision-recall analysis further indicated improved performance under class imbalance, with higher precision-recall area values across evaluated thresholds. Calibration analysis indicated closer agreement between predicted probabilities and observed outcomes for HuSHeM, while decis
Male infertility accounts for about one-third of global infertility cases. Manual assessment of sperm abnormalities through head morphology analysis encounters issues of observer variability and diagnostic discrepancies among experts. Its alternative, Computer-Assisted Semen Analysis (CASA), suffers from low-quality sperm images, small datasets, and noisy class labels. We propose a new approach for sperm head morphology classification, called SHMC-Net, which uses segmentation masks of sperm heads to guide the morphology classification of sperm images. SHMC-Net generates reliable segmentation masks using image priors, refines object boundaries with an efficient graph-based method, and trains an image network with sperm head crops and a mask network with the corresponding masks. In the intermediate stages of the networks, image and mask features are fused with a fusion scheme to better learn morphological features. To handle noisy class labels and regularize training on small datasets, SHMC-Net applies Soft Mixup to combine mixup augmentation and a loss function. We achieve state-of-the-art results on SCIAN and HuSHeM datasets, outperforming methods that use additional pre-training o
Sperm whales communicate in short sequences of clicks known as codas. We present WhAM (Whale Acoustics Model), the first transformer-based model capable of generating synthetic sperm whale codas from any audio prompt. WhAM is built by finetuning VampNet, a masked acoustic token model pretrained on musical audio, using 10k coda recordings collected over the past two decades. Through iterative masked token prediction, WhAM generates high-fidelity synthetic codas that preserve key acoustic features of the source recordings. We evaluate WhAM's synthetic codas using Fréchet Audio Distance and through perceptual studies with expert marine biologists. On downstream classification tasks including rhythm, social unit, and vowel classification, WhAM's learned representations achieve strong performance, despite being trained for generation rather than classification. Our code is available at https://github.com/Project-CETI/wham
Nowadays, computer-aided sperm analysis (CASA) systems have made a big leap in extracting the characteristics of spermatozoa for studies or measuring human fertility. The first step in sperm characteristics analysis is sperm detection in the frames of the video sample. In this article, we used RetinaNet, a deep fully convolutional neural network as the object detector. Sperms are small objects with few attributes, that makes the detection more difficult in high-density samples and especially when there are other particles in semen, which could be like sperm heads. One of the main attributes of sperms is their movement, but this attribute cannot be extracted when only one frame would be fed to the network. To improve the performance of the sperm detection network, we concatenated some consecutive frames to use as the input of the network. With this method, the motility attribute has also been extracted, and then with the help of the deep convolutional network, we have achieved high accuracy in sperm detection. The second step is tracking the sperms, for extracting the motility parameters that are essential for indicating fertility and other studies on sperms. In the tracking phase,
Sperm cooperation has evolved in a variety of taxa and is often considered a response to sperm competition, yet the benefit of this form of collective movement remains unclear. Here we use fine-scale imaging and a minimal mathematical model to study sperm aggregation in the rodent genus $Peromyscus$. We demonstrate that as the number of sperm cells in an aggregate increase, the group moves with more persistent linearity but without increasing speed; this benefit, however, is offset in larger aggregates as the geometry of the group forces sperm to swim against one another. The result is a non-monotonic relationship between aggregate size and average velocity with both a theoretically predicted and empirically observed optimum of 6-7 sperm/aggregate. To understand the role of sexual selection in driving these sperm group dynamics, we compared two sister-species with divergent mating systems and find that sperm of $P.\,maniculatus$ (highly promiscuous), which have evolved under intense competition, form optimal-sized aggregates more often than sperm of $P.\,polionotus$ (strictly monogamous), which lack competition. Our combined mathematical and experimental study of coordinated sperm
Active systems -- including sperm cells, living organisms like bacteria, fish, birds, or active soft matter systems like synthetic ''microswimmers'' -- are characterized by motility, i.e., the ability to propel using their own ''engine''. Motility is the key feature that distinguishes active systems from passive or externally driven systems. In a large ensemble, motility of individual species can vary in a wide range. Selecting active species according to their motility represents an exciting and challenging problem. We propose a new method for selecting active species based on their motility using an acoustofluidic setup where highly motile species escape from the acoustic trap. This is demonstrated in simulations and in experiments with self-propelled Janus particles and human sperm. The immediate application of this method is selecting highly motile sperm for medically assisted reproduction (MAR). Due to the tunable acoustic trap, the proposed method is more flexible than the existing passive microfluidic methods. The proposed selection method based on motility can also be applied to other active systems that require selecting highly motile species or removing immotile species.
Competition among gametes for fertilization imposes strong selection. For external fertilizers, this selective pressure extends to eggs for which spawning conditions can range from sperm limitation (competition among eggs) to sexual conflict (overabundance of competing sperm toxic to eggs). Yet existing fertilization models ignore dynamics that can alter the functional nature of gamete interactions. These factors include attraction of sperm to eggs, egg crowding effects or other nonlinearities in per capita rates of sperm-egg interaction. Such processes potentially allow egg concentrations to drastically affect viable fertilization probabilities. I experimentally tested whether such egg effects occur using the urchin $\textit{Strongylocentrotus purpuratus}$ and parameterized a newly derived model of fertilization dynamics and existing models modified to include such interactions. The experiments revealed that at low sperm concentrations, eggs compete for sperm while at high sperm concentrations eggs cooperatively reduce abnormal fertilization (a proxy for polyspermy). I show that these observations are consistent with declines in the per capita rate at which sperm and eggs interact
A key technology in sperm whale (Physeter macrocephalus) monitoring is the identification of sperm whale communication signals, known as codas. In this paper we present the first automatic coda detector and annotator. The main innovation in our detector is graph-based clustering, which utilizes the expected similarity between the clicks that make up the coda. Results show detection and accurate annotation at low signal-to-noise ratios, separation between codas and echolocation clicks, and discrimination between codas from simultaneously emitting whales. Using this automatic annotator, insights into the characterization of sperm whale communication are presented. The results include new types of coda signals, analyzes of the distribution of coda types among different whales and for different years, and evidence for synchronization between communicating whales in terms of coda type and coda transmission time. These results indicate a high degree of complexity in the communication system of this cetacean species. To ensure traceability, we share the implementation code of our coda detector.
Sperm whales (Physeter macrocephalus) navigate underwater with a series of impulsive, click-like sounds known as echolocation clicks. These clicks are characterized by a multipulse structure (MPS) that serves as a distinctive pattern. In this work, we use the stability of the MPS as a detection metric for recognizing and classifying the presence of clicks in noisy environments. To distinguish between noise transients and to handle simultaneous emissions from multiple sperm whales, our approach clusters a time series of MPS measures while removing potential clicks that do not fulfil the limits of inter-click interval, duration and spectrum. As a result, our approach can handle high noise transients and low signal-to-noise ratio. The performance of our detection approach is examined using three datasets: seven months of recordings from the Mediterranean Sea containing manually verified ambient noise; several days of manually labelled data collected from the Dominica Island containing approximately 40,000 clicks from multiple sperm whales; and a dataset from the Bahamas containing 1,203 labelled clicks from a single sperm whale. Comparing with the results of two benchmark detectors, a
The detection of tiny objects in microscopic videos is a problematic point, especially in large-scale experiments. For tiny objects (such as sperms) in microscopic videos, current detection methods face challenges in fuzzy, irregular, and precise positioning of objects. In contrast, we present a convolutional neural network for tiny object detection (TOD-CNN) with an underlying data set of high-quality sperm microscopic videos (111 videos, $>$ 278,000 annotated objects), and a graphical user interface (GUI) is designed to employ and test the proposed model effectively. TOD-CNN is highly accurate, achieving $85.60\%$ AP$_{50}$ in the task of real-time sperm detection in microscopic videos. To demonstrate the importance of sperm detection technology in sperm quality analysis, we carry out relevant sperm quality evaluation metrics and compare them with the diagnosis results from medical doctors.