Estimation of Sperm Concentration and Total Motility from Microscopic Videos of Human Semen Samples

Dewan, Karan; Dastidar, Tathagato Rai; Ahmad, Maroof

doi:10.1109/cvprw.2018.00307

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…In the dataset paper, the authors presented baseline mean absolute error values for motility and morphology. Moreover, the importance of computeraided sperm analysis can be identified from the research works which have been done to develop automatic sperm analysis method in last few decades [3,13,19].…”

Section: Introductionmentioning

confidence: 99%

Automatic Tracking and Motility Analysis of Human Sperm in Time-Lapse Images

Urbano

Masson

VerMilyea

et al. 2017

IEEE Trans. Med. Imaging

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We present a fully automated multi-sperm tracking algorithm. It has the demonstrated capability to detect and track simultaneously hundreds of sperm cells in recorded videos while accurately measuring motility parameters over time and with minimal operator intervention. Algorithms of this kind may help in associating dynamic swimming parameters of human sperm cells with fertility and fertilization rates. Specifically, we offer 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. Adapting the well-known joint probabilistic data association filter (JPDAF), we automatically tracked hundreds of human sperm simultaneously and measured their dynamic swimming parameters over time. Unlike existing CASA instruments, our algorithm has the capability to track sperm swimming in close proximity to each other and during apparent cell-to-cell collisions. Collecting continuously parameters for each sperm tracked without sample dilution (currently impossible using standard CASA systems) provides an opportunity to compare such data with standard fertility rates. The use of our algorithm thus has the potential to free the clinician from having to rely on elaborate motility measurements obtained manually by technicians, speed up semen processing, and provide medical practitioners and researchers with more useful data than are currently available.

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Section: Introductionmentioning

confidence: 99%

Automatic Tracking and Motility Analysis of Human Sperm in Time-Lapse Images

Urbano

Masson

VerMilyea

et al. 2017

IEEE Trans. Med. Imaging

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“…On the publicly available Modified Human Sperm Morphology Analysis Dataset (MHSMA) dataset derived from Human Sperm Morphology Analysis Dataset (HSMA-DS) [36], these networks have been applied by Javadi et al [37] to perform automatic morphology classification of spermatozoa images and achieved high detection accuracy for head, vacuole, and tail defect. Dewan et al [38] further integrated CNN classifiers into their automatic sperm analysis framework as a means to filter out tracks belonging to non sperm particles, such as debris. Applying transfer learning from an ImageNet pre-trained VGG16 network has also been applied to the classification of sperm morphology from individual annotated images [39].…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning Framework for Automatic Prediction of Human Semen Motility

Ottl¹,

Amiriparian²,

Gerczuk³

et al. 2021

Preprint

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In the field of reproductive health, a vital aspect for the detection of male fertility issues is the analysis of human semen quality. Two factors of importance are the morphology and motility of the sperm cells. While the former describes defects in different parts of a spermatozoon, the latter measures the efficient movement of cells. For many non-human species, so-called Computer-Aided Sperm Analysis systems work well for assessing these characteristics from microscopic video recordings but struggle with human sperm samples which generally show higher degrees of debris and dead spermatozoa, as well as lower overall sperm motility. Here, machine learning methods that harness large amounts of training data to extract salient features could support physicians with the detection of fertility issues or in vitro fertilisation procedures. In this work, the overall motility of given sperm samples is predicted with the help of a machine learning framework integrating unsupervised methods for feature extraction with downstream regression models. The models evaluated herein improve on the state-of-the-art for video-based sperm-motility prediction.

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“…It is a typical assumption that visual analysis, as provided by the computer vision and medical image processing communities today, is capable of already providing viable and practical approaches to healthcare multimedia challenges. Automatic analysis of human semen is an active field of research supported by several studies on the topic [1,3,6,7,11,15,17]. However, a common theme is that approaches usually focus on one modality and do not incorporate other data sources into their analysis.…”

Section: Introductionmentioning

confidence: 99%

ACM Multimedia BioMedia 2020 Grand Challenge Overview

Hicks

Thambawita

Hammer

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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The BioMedia 2020 ACM Multimedia Grand Challenge is the second in a series of competitions focusing on the use of multimedia for different medical use-cases. In this year's challenge, participants are asked to develop algorithms that automatically predict the quality of a given human semen sample using a combination of visual, patient-related, and laboratory-analysis-related data. Compared to last year's challenge, participants are provided with a fully multimodal dataset (videos, analysis data, study participant data) from the field of assisted human reproduction. The tasks encourage the use of the different modalities contained within the dataset and finding smart ways of how they may be combined to further improve prediction accuracy. For example, using only video data or combining video data and patient-related data. The ground truth was developed through a preliminary analysis done by medical experts following the World Health Organization's standard for semen quality assessment. The task lays the basis for automatic, real-time support systems for artificial reproduction. We hope that this challenge motivates multimedia researchers to explore more medical-related applications and use their vast knowledge to make a real impact on people's lives.

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Estimation of Sperm Concentration and Total Motility from Microscopic Videos of Human Semen Samples

Cited by 9 publications

References 14 publications

Automatic Tracking and Motility Analysis of Human Sperm in Time-Lapse Images

Automatic Tracking and Motility Analysis of Human Sperm in Time-Lapse Images

A Machine Learning Framework for Automatic Prediction of Human Semen Motility

ACM Multimedia BioMedia 2020 Grand Challenge Overview

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