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Fast Noninvasive Morphometric Characterization of Free Human Sperms Using Deep Learning

Published online by Cambridge University Press:  24 June 2022

Guole Liu
Affiliation:
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Hao Shi
Affiliation:
Sperm Capturer (Beijing) Biotechnology Co. Ltd., Beijing 100070, China
Huan Zhang
Affiliation:
Reproductive Medicine Center, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang 325000, China
Yating Zhou
Affiliation:
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Yujiao Sun
Affiliation:
State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
Wei Li
Affiliation:
Beijing Children's Hospital, Capital Medical University, Beijing 100045, China
Xuefeng Huang
Affiliation:
Reproductive Medicine Center, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang 325000, China
Yuqiang Jiang
Affiliation:
State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
Yaliang Fang*
Affiliation:
Sperm Capturer (Beijing) Biotechnology Co. Ltd., Beijing 100070, China
Ge Yang*
Affiliation:
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
*
*Corresponding author: Ge Yang, Email: yangge@ucas.edu.cn; Yaliang Fang, Email: fangyaliang@spermcapturer.com
*Corresponding author: Ge Yang, Email: yangge@ucas.edu.cn; Yaliang Fang, Email: fangyaliang@spermcapturer.com

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.

Type
Biological Applications
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of the Microscopy Society of America

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