An algorithm for Morphological classification of motile human sperm

Raemdonck, Lore E.M. Van; ur-Rehman, Ata; Garcia, Maria Luisa Davila; Mihaylova, Lyudmila; Harrison, Robert F.; Pacey, Allan

doi:10.1109/sdf.2015.7347714

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…It distinguishes their work from other existing methods that analyze the motility of sperm cells based simply on the head's trajectories. Later, Van Raemdonck [5] implemented a computer-aided sperm cell analysis algorithm to accurately and efficiently analyze these cells' morphology. First, the authors remove the background, then cell segmentation is successfully used to sort cells into motile and immobile cells first, and motile cells sort into normal and abnormal cells second.…”

Section: Related Workmentioning

confidence: 99%

Sperm Cell Segmentation in Digital Micrographs based on Convolutional Neural Networks using U-Net Architecture

Meléndez

Castañón

Medina-Rodríguez

2021

2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS)

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Human infertility is considered a serious disease of the reproductive system that affects more than 10% of couples worldwide, and more than 30% of reported cases are related to men. The crucial step in evaluating male infertility is a semen analysis, highly dependent on sperm morphology. However, this analysis is done at the laboratory manually and depends mainly on the doctor's experience. Besides, it is laborious, and there is also a high degree of interlaboratory variability in the results. This article proposes applying a specialized convolutional neural network architecture (U-Net), which focuses on the segmentation of sperm cells in micrographs to overcome these problems. The results showed high scores for the model segmentation metrics such as precision (93%), IoU score (86%), and DICE score of 93%. Moreover, we can conclude that U-net architecture turned out to be a good option to carry out the segmentation of sperm cells.

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Section: Related Workmentioning

confidence: 99%

Sperm Cell Segmentation in Digital Micrographs based on Convolutional Neural Networks using U-Net Architecture

Meléndez

Castañón

Medina-Rodríguez

2021

2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS)

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Machine learning for sperm selection

et al. 2021

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A review of different deep learning techniques for sperm fertility prediction

Suleman

Ilyas

Lali

et al. 2023

MATH

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<abstract> <p>Sperm morphology analysis (SMA) is a significant factor in diagnosing male infertility. Therefore, healthy sperm detection is of great significance in this process. However, the traditional manual microscopic sperm detection methods have the disadvantages of a long detection cycle, low detection accuracy in large orders, and very complex fertility prediction. Therefore, it is meaningful to apply computer image analysis technology to the field of fertility prediction. Computer image analysis can give high precision and high efficiency in detecting sperm cells. In this article, first, we analyze the existing sperm detection techniques in chronological order, from traditional image processing and machine learning to deep learning methods in segmentation and classification. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. Finally, the future development direction and challenges of sperm cell detection are discussed. We have summarized 44 related technical papers from 2012 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of fertility prediction and provide a reference for researchers in other fields.</p> </abstract>

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An algorithm for Morphological classification of motile human sperm

Abstract: This is a repository copy of Human Sperm Morphology Analysis for Assisted Reproduction Techniques.

Cited by 3 publications

References 15 publications

Sperm Cell Segmentation in Digital Micrographs based on Convolutional Neural Networks using U-Net Architecture

Sperm Cell Segmentation in Digital Micrographs based on Convolutional Neural Networks using U-Net Architecture

Machine learning for sperm selection

A review of different deep learning techniques for sperm fertility prediction

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