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基于深度学习的人工授精精子活力评估。

Deep Learning Based Evaluation of Spermatozoid Motility for Artificial Insemination.

机构信息

Department of Control Systems, Kaunas University of Technology, 51423 Kaunas, Lithuania.

Department of Multimedia Engineering, Kaunas University of Technology, 51423 Kaunas, Lithuania.

出版信息

Sensors (Basel). 2020 Dec 24;21(1):72. doi: 10.3390/s21010072.

DOI:10.3390/s21010072
PMID:33374461
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7795243/
Abstract

We propose a deep learning method based on the Region Based Convolutional Neural Networks (R-CNN) architecture for the evaluation of sperm head motility in human semen videos. The neural network performs the segmentation of sperm heads, while the proposed central coordinate tracking algorithm allows us to calculate the movement speed of sperm heads. We have achieved 91.77% (95% CI, 91.11-92.43%) accuracy of sperm head detection on the VISEM (A Multimodal Video Dataset of Human Spermatozoa) sperm sample video dataset. The mean absolute error (MAE) of sperm head vitality prediction was 2.92 (95% CI, 2.46-3.37), while the Pearson correlation between actual and predicted sperm head vitality was 0.969. The results of the experiments presented below will show the applicability of the proposed method to be used in automated artificial insemination workflow.

摘要

我们提出了一种基于区域卷积神经网络（R-CNN）架构的深度学习方法，用于评估人类精液视频中的精子头运动。该神经网络执行精子头的分割，而我们提出的中心坐标跟踪算法允许我们计算精子头的运动速度。我们在 VISEM（人类精子的多模态视频数据集）精子样本视频数据集上实现了 91.77%（95%置信区间，91.11-92.43%）的精子头检测准确率。精子头活力预测的平均绝对误差（MAE）为 2.92（95%置信区间，2.46-3.37），而实际和预测的精子头活力之间的皮尔逊相关系数为 0.969。下面呈现的实验结果将表明所提出的方法在自动化人工授精工作流程中的适用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/349a31760cfa/sensors-21-00072-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/280acebd9dbf/sensors-21-00072-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/e9de463f8857/sensors-21-00072-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/2e38cdaf48e8/sensors-21-00072-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/8e864dd806de/sensors-21-00072-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/b1f0c67267da/sensors-21-00072-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/be49a4ccb1fa/sensors-21-00072-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/ab13f7db9d83/sensors-21-00072-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/b801c297a8ce/sensors-21-00072-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/674c5d9b8ac9/sensors-21-00072-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/69db43d9996f/sensors-21-00072-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/6e379cd98f81/sensors-21-00072-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/7e0c3ab00219/sensors-21-00072-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/349a31760cfa/sensors-21-00072-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/280acebd9dbf/sensors-21-00072-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/e9de463f8857/sensors-21-00072-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/2e38cdaf48e8/sensors-21-00072-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/8e864dd806de/sensors-21-00072-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/b1f0c67267da/sensors-21-00072-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/be49a4ccb1fa/sensors-21-00072-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/ab13f7db9d83/sensors-21-00072-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/b801c297a8ce/sensors-21-00072-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/674c5d9b8ac9/sensors-21-00072-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/69db43d9996f/sensors-21-00072-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/6e379cd98f81/sensors-21-00072-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/7e0c3ab00219/sensors-21-00072-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e022/7795243/349a31760cfa/sensors-21-00072-g013.jpg

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引用本文的文献

[1]
[Advances in the Application of Artificial Intelligence in the Field of Male Infertility].

Sichuan Da Xue Xue Bao Yi Xue Ban. 2025-3-20

[2]
Automatic identification of human spermatozoa with zona pellucida-binding capability using deep learning.

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[3]
Artificial Intelligence, Clinical Decision Support Algorithms, Mathematical Models, Calculators Applications in Infertility: Systematic Review and Hands-On Digital Applications.

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[4]
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[5]
Artificial Intelligence for Clinical Management of Male Infertility, a Scoping Review.

Curr Urol Rep. 2024-11-9

[6]
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[7]
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[8]
WISE: whole-scenario embryo identification using self-supervised learning encoder in IVF.

J Assist Reprod Genet. 2024-4

[9]
Current Updates on Involvement of Artificial Intelligence and Machine Learning in Semen Analysis.

Medicina (Kaunas). 2024-2-6

[10]
DTLR-CS: Deep tensor low rank channel cross fusion neural network for reproductive cell segmentation.

PLoS One. 2023

本文引用的文献

[1]
DeepSperm: A robust and real-time bull sperm-cell detection in densely populated semen videos.

Comput Methods Programs Biomed. 2021-9

[2]
Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists.

Diagnostics (Basel). 2020-8-6

[3]
High spatially sensitive quantitative phase imaging assisted with deep neural network for classification of human spermatozoa under stressed condition.

Sci Rep. 2020-8-4

[4]
Reproductive outcomes predicted by phase imaging with computational specificity of spermatozoon ultrastructure.

Proc Natl Acad Sci U S A. 2020-7-20

[5]
Automated sperm morphology analysis approach using a directional masking technique.

Comput Biol Med. 2020-7

[6]
Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data.

J Assist Reprod Genet. 2020-10

[7]
Deep Learning-Based Morphological Classification of Human Sperm Heads.

Diagnostics (Basel). 2020-5-20

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A fully automated hybrid human sperm detection and classification system based on mobile-net and the performance comparison with conventional methods.

Med Biol Eng Comput. 2020-5

[9]
Learning physical properties in complex visual scenes: An intelligent machine for perceiving blood flow dynamics from static CT angiography imaging.

Neural Netw. 2019-11-30

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Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction.

Sci Rep. 2019-11-14

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