中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
  5. 计算生物学与机器智能
Fast Noninvasive Morphometric Characterization of Free Human Sperms Using Deep Learning

文献类型：期刊论文

作者Liu, Guole5,6; Shi, Hao4; Zhang, Huan3; Zhou, Yating5,6; Sun, Yujiao2; Li, Wei1; Huang, Xuefeng3; Jiang, Yuqiang2; Fang, Yaliang4; Yang, Ge5,6
刊名MICROSCOPY AND MICROANALYSIS
出版日期2022-06-24
页码13
ISSN号1431-9276
关键词bright-field microscopy deep learning human sperm intracytoplasmic sperm injection sperm morphology
DOI10.1017/S1431927622012132
通讯作者Fang, Yaliang(yangge@ucas.edu.cn) ; Yang, Ge(fangyaliang@spermcapturer.com)
英文摘要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 mu m 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.
WOS关键词MORPHOLOGY ; FERTILIZATION ; SEGMENTATION ; MOTILITY ; IMAGES
资助项目National Natural Science Foundation of China[91954201] ; National Natural Science Foundation of China[31971289] ; Chinese Academy of Sciences[292019000056] ; University of Chinese Academy of Sciences[115200M001] ; Beijing Municipal Science & Technology Commission[Z181100001918016]
WOS研究方向Materials Science ; Microscopy
语种英语
出版者CAMBRIDGE UNIV PRESS
WOS记录号WOS:000815545000001
资助机构National Natural Science Foundation of China ; Chinese Academy of Sciences ; University of Chinese Academy of Sciences ; Beijing Municipal Science & Technology Commission
源URL[http://ir.ia.ac.cn/handle/173211/49234]  
专题模式识别国家重点实验室_计算生物学与机器智能
通讯作者Fang, Yaliang; Yang, Ge
作者单位1.Capital Med Univ, Beijing Childrens Hosp, Beijing 100045, Peoples R China
2.Chinese Acad Sci, Inst Genet & Dev Biol, State Key Lab Mol Dev Biol, Beijing 100101, Peoples R China
3.Wenzhou Med Univ, Affiliated Hosp 1, Reprod Med Ctr, Wenzhou 325000, Zhejiang, Peoples R China
4.Sperm Capturer Beijing Biotechnol Co Ltd, Beijing 100070, Peoples R China
5.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Guole,Shi, Hao,Zhang, Huan,et al. Fast Noninvasive Morphometric Characterization of Free Human Sperms Using Deep Learning[J]. MICROSCOPY AND MICROANALYSIS,2022:13.
APA Liu, Guole.,Shi, Hao.,Zhang, Huan.,Zhou, Yating.,Sun, Yujiao.,...&Yang, Ge.(2022).Fast Noninvasive Morphometric Characterization of Free Human Sperms Using Deep Learning.MICROSCOPY AND MICROANALYSIS,13.
MLA Liu, Guole,et al."Fast Noninvasive Morphometric Characterization of Free Human Sperms Using Deep Learning".MICROSCOPY AND MICROANALYSIS (2022):13.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。