Fast Noninvasive Morphometric Characterization of Free Human Sperms Using Deep Learning
Guole Liu1,2; Hao Shi3; Huan Zhang4; Yating Zhou1,2; Yujiao Sun5; Wei Li6; Xuefeng Huang4; Yuqiang Jiang5; Yaliang Fang3; Ge Yang1,2
Source PublicationMicroscopy and Microanalysis
2022
Volume28Issue:5Pages:1767-1779
Abstract

The selection of high-quality sperms is critical to intracytoplasmic sperm injection, which accounts for 70–80% of in vitro fertilization (IVF) treatments. So far, sperm screening is usually performed manually by clinicians. However, the performance of manual screening is limited in its objectivity, consistency, and efficiency. To overcome these limitations, we have developed a fast and noninvasive three-stage method to characterize morphology of freely swimming human sperms in bright-field microscopy images using deep learning models. Specifically, we use an object detection model to identify sperm heads, a classification model to select in-focus images, and a segmentation model to extract geometry of sperm heads and vacuoles. The models achieve an F1-score of 0.951 in sperm head detection, a z-position estimation error within ±1.5 μm in in-focus image selection, and a Dice score of 0.948 in sperm head segmentation, respectively. Customized lightweight architectures are used for the models to achieve real-time analysis of 200 frames per second. Comprehensive morphological parameters are calculated from sperm head geometry extracted by image segmentation. Overall, our method provides a reliable and efficient tool to assist clinicians in selecting high-quality sperms for successful IVF. It also demonstrates the effectiveness of deep learning in real-time analysis of live bright-field microscopy images.

Keywordbright-field microscopy deep learning human sperm intracytoplasmic sperm injection sperm morphology
Indexed BySCIE
Language英语
Sub direction classification计算智能
planning direction of the national heavy laboratoryAI For Science
Paper associated data
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57358
Collection多模态人工智能系统全国重点实验室_计算生物学与机器智能
Corresponding AuthorYaliang Fang; Ge Yang
Affiliation1.School of Artificial Intelligence, University of Chinese Academy of Sciences
2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
3.Sperm Capturer (Beijing) Biotechnology Co. Ltd.
4.Reproductive Medicine Center, The First Affiliated Hospital of Wenzhou Medical University
5.State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology
6.Beijing Children’s Hospital, Capital Medical University
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Guole Liu,Hao Shi,Huan Zhang,et al. Fast Noninvasive Morphometric Characterization of Free Human Sperms Using Deep Learning[J]. Microscopy and Microanalysis,2022,28(5):1767-1779.
APA Guole Liu.,Hao Shi.,Huan Zhang.,Yating Zhou.,Yujiao Sun.,...&Ge Yang.(2022).Fast Noninvasive Morphometric Characterization of Free Human Sperms Using Deep Learning.Microscopy and Microanalysis,28(5),1767-1779.
MLA Guole Liu,et al."Fast Noninvasive Morphometric Characterization of Free Human Sperms Using Deep Learning".Microscopy and Microanalysis 28.5(2022):1767-1779.
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