Knowledge Commons of Institute of Automation,CAS
Fast Noninvasive Morphometric Characterization of Free Human Sperms Using Deep Learning | |
Guole Liu1,2![]() ![]() ![]() | |
发表期刊 | Microscopy and Microanalysis
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2022 | |
卷号 | 28期号:5页码:1767-1779 |
摘要 | 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. |
关键词 | bright-field microscopy deep learning human sperm intracytoplasmic sperm injection sperm morphology |
收录类别 | SCIE |
语种 | 英语 |
七大方向——子方向分类 | 计算智能 |
国重实验室规划方向分类 | AI For Science |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57358 |
专题 | 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 |
通讯作者 | Yaliang Fang; Ge Yang |
作者单位 | 1.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 |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 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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