Alexandra Keller, McKenna Maus, Emma Keller, Karl Kerns
{"title":"用于公猪精子形态分析的深度学习分类方法","authors":"Alexandra Keller, McKenna Maus, Emma Keller, Karl Kerns","doi":"10.1111/andr.13758","DOIUrl":null,"url":null,"abstract":"BackgroundBoar semen quality emphasizes three major criteria: sperm concentration, motility, and morphology. Methods to analyze concentration and motility quickly and objectively readily exist, but few exist for analyzing morphology outside of subjective manual counting. Other vital factors for fertilization, like acrosome health, lack efficient detection methods due to limitations in detection by the human eye and costly biomarker analysis, which is rarely used in semen diagnostics.ObjectiveTo overcome these challenges, we propose a novel approach integrating deep‐learning technology with high‐throughput image‐based flow cytometry (IBFC) for objective and accurate analysis of both morphology and label‐free acrosome health of thousands of individual spermatozoa at once, as opposed to manually counting on a microscope slide.Materials and methodsImages of 10,000 spermatozoa were captured using an IBFC and manually annotated based on the primary morphological defect or acrosome health status for the training of the convolutional neural network (CNN). The CNN used these images to train and then applied that training to unannotated images to predict the model accuracy.ResultsUsing the CNNs, high F1 scores of 96.73%, 98.55%, and 99.31% for 20x, 40x, and 60x magnifications, respectively, for morphological classification were attained. Additionally, the model demonstrates an F1 score of 99.8% in detecting subtle acrosome health variations at the 60x magnification.Discussion and conclusionsWe have established an integrated approach to rapidly collect and classify morphological defects and acrosome health status, without the use of manual counting or biomarker labeling. Our study underscores the potential of artificial intelligence in semen diagnostics, reducing technician variability, streamlining assays, and facilitating the development of additional label‐free detection methods. This innovative approach addresses the barriers hindering biomarker adoption in semen analysis, offering a promising avenue for enhancing reproductive health assessments.","PeriodicalId":7898,"journal":{"name":"Andrology","volume":"37 1","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep learning classification method for boar sperm morphology analysis\",\"authors\":\"Alexandra Keller, McKenna Maus, Emma Keller, Karl Kerns\",\"doi\":\"10.1111/andr.13758\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"BackgroundBoar semen quality emphasizes three major criteria: sperm concentration, motility, and morphology. Methods to analyze concentration and motility quickly and objectively readily exist, but few exist for analyzing morphology outside of subjective manual counting. Other vital factors for fertilization, like acrosome health, lack efficient detection methods due to limitations in detection by the human eye and costly biomarker analysis, which is rarely used in semen diagnostics.ObjectiveTo overcome these challenges, we propose a novel approach integrating deep‐learning technology with high‐throughput image‐based flow cytometry (IBFC) for objective and accurate analysis of both morphology and label‐free acrosome health of thousands of individual spermatozoa at once, as opposed to manually counting on a microscope slide.Materials and methodsImages of 10,000 spermatozoa were captured using an IBFC and manually annotated based on the primary morphological defect or acrosome health status for the training of the convolutional neural network (CNN). The CNN used these images to train and then applied that training to unannotated images to predict the model accuracy.ResultsUsing the CNNs, high F1 scores of 96.73%, 98.55%, and 99.31% for 20x, 40x, and 60x magnifications, respectively, for morphological classification were attained. Additionally, the model demonstrates an F1 score of 99.8% in detecting subtle acrosome health variations at the 60x magnification.Discussion and conclusionsWe have established an integrated approach to rapidly collect and classify morphological defects and acrosome health status, without the use of manual counting or biomarker labeling. Our study underscores the potential of artificial intelligence in semen diagnostics, reducing technician variability, streamlining assays, and facilitating the development of additional label‐free detection methods. This innovative approach addresses the barriers hindering biomarker adoption in semen analysis, offering a promising avenue for enhancing reproductive health assessments.\",\"PeriodicalId\":7898,\"journal\":{\"name\":\"Andrology\",\"volume\":\"37 1\",\"pages\":\"\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Andrology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1111/andr.13758\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ANDROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Andrology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/andr.13758","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ANDROLOGY","Score":null,"Total":0}
引用次数: 0
摘要
背景公猪精液质量强调三个主要标准:精子浓度、活力和形态。目前已有快速客观地分析精子浓度和活力的方法,但除了主观的人工计数外,很少有分析形态的方法。为了克服这些挑战,我们提出了一种将深度学习技术与高通量图像流式细胞术(IBFC)相结合的新方法,可一次性客观准确地分析数千个精子的形态和无标记顶体健康状况,而无需在显微镜载玻片上手动计数。材料与方法使用 IBFC 采集 10,000 个精子的图像,并根据主要形态缺陷或顶体健康状况进行人工标注,用于训练卷积神经网络(CNN)。结果使用卷积神经网络,在 20 倍、40 倍和 60 倍放大率下,形态分类的 F1 分数分别达到 96.73%、98.55% 和 99.31%。此外,该模型在 60x 放大倍率下检测细微顶体健康变化的 F1 得分为 99.8%。讨论与结论我们建立了一种综合方法,可快速收集形态缺陷和顶体健康状况并进行分类,而无需使用人工计数或生物标记标记。我们的研究强调了人工智能在精液诊断中的潜力,它可以减少技术人员的变异性,简化检测过程,促进更多无标记检测方法的开发。这种创新方法解决了精液分析中采用生物标记物的障碍,为加强生殖健康评估提供了一条前景广阔的途径。
Deep learning classification method for boar sperm morphology analysis
BackgroundBoar semen quality emphasizes three major criteria: sperm concentration, motility, and morphology. Methods to analyze concentration and motility quickly and objectively readily exist, but few exist for analyzing morphology outside of subjective manual counting. Other vital factors for fertilization, like acrosome health, lack efficient detection methods due to limitations in detection by the human eye and costly biomarker analysis, which is rarely used in semen diagnostics.ObjectiveTo overcome these challenges, we propose a novel approach integrating deep‐learning technology with high‐throughput image‐based flow cytometry (IBFC) for objective and accurate analysis of both morphology and label‐free acrosome health of thousands of individual spermatozoa at once, as opposed to manually counting on a microscope slide.Materials and methodsImages of 10,000 spermatozoa were captured using an IBFC and manually annotated based on the primary morphological defect or acrosome health status for the training of the convolutional neural network (CNN). The CNN used these images to train and then applied that training to unannotated images to predict the model accuracy.ResultsUsing the CNNs, high F1 scores of 96.73%, 98.55%, and 99.31% for 20x, 40x, and 60x magnifications, respectively, for morphological classification were attained. Additionally, the model demonstrates an F1 score of 99.8% in detecting subtle acrosome health variations at the 60x magnification.Discussion and conclusionsWe have established an integrated approach to rapidly collect and classify morphological defects and acrosome health status, without the use of manual counting or biomarker labeling. Our study underscores the potential of artificial intelligence in semen diagnostics, reducing technician variability, streamlining assays, and facilitating the development of additional label‐free detection methods. This innovative approach addresses the barriers hindering biomarker adoption in semen analysis, offering a promising avenue for enhancing reproductive health assessments.
期刊介绍:
Andrology is the study of the male reproductive system and other male gender related health issues. Andrology deals with basic and clinical aspects of the male reproductive system (gonads, endocrine and accessory organs) in all species, including the diagnosis and treatment of medical problems associated with sexual development, infertility, sexual dysfunction, sex hormone action and other urological problems. In medicine, Andrology as a specialty is a recent development, as it had previously been considered a subspecialty of urology or endocrinology